With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource corpora have systematic issues: At least 15 corpora have no usable text, and a significant fraction contains less than 50% sentences of acceptable quality. In addition, many are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-proficient speakers, and supplement the human audit with automatic analyses. Finally, we recommend techniques to evaluate and improve multilingual corpora and discuss potential risks that come with low-quality data releases.

Access to multilingual datasets for NLP research has vastly improved over the past years. A variety of Web-derived collections for hundreds of languages is available for anyone to download, such as ParaCrawl (Esplà et al., 2019; Bañón et al., 2020), WikiMatrix (Schwenk et al., 2021), CCAligned (El-Kishky et al., 2020), OSCAR (Ortiz Suárez et al., 2019; Ortiz Suárez et al., 2020), and several others. These have in turn enabled a variety of highly multilingual models, like mT5 (Xue et al., 2021), M2M-100 (Fan et al., 2020), and M4 (Arivazhagan et al., 2019).

Curating such datasets relies on the Web sites giving clues about the language of their contents (e.g., a language identifier in the URL) and on automatic language classification (LangID). It is commonly known that these automatically crawled and filtered datasets tend to have overall lower quality than hand-curated collections (Koehn et al., 2020), but their quality is rarely measured directly, and is rather judged through the improvements they bring to downstream applications (Schwenk et al., 2021).

Building NLP technologies with automatically crawled datasets is promising. This is especially true for low-resource languages, because data scarcity is one of the major bottlenecks for deep learning approaches. However, there is a problem: There exists very little research on evaluating both data collections and automatic crawling and filtering tools for low-resource languages. As a result, although many low-resource languages are covered by the latest multilingual crawl data releases, their quality and thus usability is unknown.

To shed light on the quality of data crawls for the lowest resource languages, we perform a manual data audit for 230 per-language subsets of five major crawled multilingual datasets:1 CCAligned (El-Kishky et al., 2020), ParaCrawl (Esplà et al., 2019; Bañón et al., 2020), WikiMatrix (Schwenk et al., 2021), OSCAR (Ortiz Suárez et al., 2019; Ortiz Suárez et al., 2020), and mC4 (Xue et al., 2021). We propose solutions for effective, low-effort data auditing (Section 4), including an error taxonomy. Our quantitative analysis reveals surprisingly low amounts of valid in-language data, and identifies systematic issues across datasets and languages. In addition, we find that a large number of datasets is labeled with nontransparent or incorrect language codes (Section 5). This leads us to reflect on the potential harm of low-quality data releases for low-resource languages (Section 6), and provide a set of recommendations for future multilingual data releases (Section 7).

Corpora collected by web crawlers are known to be noisy (Junczys-Dowmunt, 2019; Luccioni and Viviano, 2021). In highly multilingual settings, past work found that web-crawls of lower-resource languages have serious issues, especially with segment-level LangID (Caswell et al., 2020). Cleaning and filtering web-crawls can boost general language modeling (Gao et al., 2020; Brown et al., 2020; Raffel et al., 2020) and downstream task performance (Moore and Lewis, 2010; Rarrick et al., 2011; Xu and Koehn, 2017; Khayrallah and Koehn, 2018; Brown et al., 2020).

As the scale of ML research grows, it becomes increasingly difficult to validate automatically collected and curated datasets (Biderman and Scheirer, 2020; Birhane and Prabhu, 2021; Bender et al., 2021). Several works have focused on advancing methodologies and best practices to address these challenges. Bender and Friedman (2018) introduced data statements, a documentary framework for NLP datasets that seeks to provide a universal minimum bar for dataset description. Similar work has been done on systematizing documentation in other areas in data science and machine learning, including work focusing on online news (Kevin et al., 2018), data ethics (Sun et al., 2019), and data exploration (Holland et al., 2018), as well as generalist work such as Gebru et al. (2018). Data quality is also implicitly documented by successes of filtering methods. There is a large literature on filtering data for various NLP tasks, for example, Axelrod et al., 2011et al., 2011, Moore and Lewis (2010), Rarrick et al., 2011, 2011, Wang et al. (2018), Kamholz et al. (2014), Junczys-Dowmunt (2018), and Caswell et al., 2020, 2020.

Closest to our work is the analysis of a highly multilingual (non-publicly available) web-crawl and LangID-related quality issues by (Caswell et al., 2020). They perform a brief analysis of the quality of OSCAR with the focus only on the presence of in-language content. Dodge et al. (2021) automatically documented and analyzed the contents and sources of C4 (Raffel et al., 2020), the English counterpart of mC4, which surfaced the presence of machine-translated contents and NLP benchmark data.

Table 1 provides an overview of the corpora of interest in this work. We selected the corpora for their multilinguality and the inclusion of understudied languages in NLP. With the exception of WikiMatrix and ParaCrawl, all corpora are derived from CommonCrawl (CC).2

Table 1:

Comparison of parallel and monolingual corpora extracted from web documents, including their downstream evaluation tasks. All parallel corpora are evaluated for machine translation (BLEU). TED-6: da, cr, sl, sk, lt, et; TED-45: 45-language subset of (Qi et al., 2018); WMT-5: cs, de, fi, lv, ro. POS/DEP-5: part-of-speech labeling and dependency parsing for bg, ca, da, fi, id.

ParallelMonolingual
CCAlignedParaCrawl v7.1WikiMatrixOSCARmC4
#languages 137 41 85 166 101
Source CC 2013–2020 selected Web sites Wikipedia CC 11/2018 CC all
Filtering level document sentence sentence document document
Langid FastText CLD2 FastText FastText CLD3
Alignment LASER Vec/Hun/BLEU-Align LASER – –
Evaluation TED-6 WMT-5 TED-45 POS/DEP-5 XTREME
ParallelMonolingual
CCAlignedParaCrawl v7.1WikiMatrixOSCARmC4
#languages 137 41 85 166 101
Source CC 2013–2020 selected Web sites Wikipedia CC 11/2018 CC all
Filtering level document sentence sentence document document
Langid FastText CLD2 FastText FastText CLD3
Alignment LASER Vec/Hun/BLEU-Align LASER – –
Evaluation TED-6 WMT-5 TED-45 POS/DEP-5 XTREME
##### CCAligned (El-Kishky et al., 2020)

is a parallel dataset built off 68 CC snapshots. Documents are aligned if they are in the same language according to FastText LangID (Joulin et al., 2016, 2017), and have the same URL but for a differing language code. These alignments are refined with cross-lingual LASER embeddings (Artetxe and Schwenk, 2019). For sentence-level data, they split on newlines and align with LASER, but perform no further filtering. Human annotators evaluated the quality of document alignments for six languages (de, zh, ar, ro, et, my) selected for their different scripts and amount of retrieved documents, reporting precision of over 90%. The quality of the extracted parallel sentences was evaluated in a machine translation (MT) task on six European (da, cr, sl, sk, lt, et) languages of the TED corpus (Qi et al., 2018), where it compared favorably to systems built on crawled sentences from WikiMatrix and ParaCrawl v6.

##### Multilingual C4 (mC4) (Xue et al., 2021)

is a document-level dataset used for training the mT5 language model. It consists of monolingual text in 101 languages and is generated from 71 CC snapshots. It filters out pages that contain less than three lines of at least 200 characters and pages that contain bad words.3 Since this is a document-level dataset, we split it by sentence and deduplicate it before rating. For language identification, it uses CLD3 (Botha et al., 2017),4 a small feed-forward neural network that was trained to detect 107 languages. The mT5 model pre-trained on mC4 is evaluated on 6 tasks of the XTREME benchmark (Hu et al., 2020) covering a variety of languages and outperforms other multilingual pre-trained language models such as mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020).

##### OSCAR (Ortiz Suárez et al., 2019; Ortiz Suárez et al., 2020)

is a set of monolingual corpora extracted from CC snapshots, specifically from the plain text WET format distributed by CC which removes all the HTML tags and converts the text to UTF-8. It is deduplicated and follows the approach by Grave et al. (2018) of using FastText LangID (Joulin et al., 2016, 2017) on a line-level.5 No other filtering was applied. For five languages (bg, ca, da, fi, id), OSCAR was used by its original authors to train language models which were then evaluated on parsing and POS tagging (Ortiz Suárez et al., 2020). OSCAR has also been used in independent studies to train monolingual or multilingual language models (ar, as, bn, de, el, fr, gu, he, hi, kn, ml, mr, nl, or, pa, ro, ta, te) and subsequently evaluate them on various downstream tasks (Antoun et al., 2021; Kakwani et al., 2020; Wilie et al., 2020; Chan et al., 2020; Koutsikakis et al., 2020; Martin et al., 2020; Chriqui and Yahav, 2021; Seker et al., 2021; Delobelle et al., 2020; Dumitrescu et al., 2020; Masala et al., 2020).

##### ParaCrawl v7.1

is a parallel dataset with 41 language pairs primarily aligned with English (39 out of 41) and mined using the parallel-data-crawling tool Bitextor (Esplà et al., 2019; Bañón et al., 2020) which includes downloading documents, preprocessing and normalization, aligning documents and segments, and filtering noisy data via Bicleaner.6 ParaCrawl focuses on European languages, but also includes 9 lower-resource, non-European language pairs in v7.1. Sentence alignment and sentence pair filtering choices were optimized for five languages (mt, et, hu, cs, de) by training and evaluating MT models on the resulting parallel sentences. An earlier version (v5) was shown to improve translation quality on WMT benchmarks for cs, de, fi, lv, ro.

##### WikiMatrix (Schwenk et al., 2021)

is a public dataset containing 135M parallel sentences in 1620 language pairs (85 languages) mined from Wikipedia. Out of the 135M parallel sentences, 34M are aligned with English. The text is extracted from Wikipedia pages, split into sentences, and duplicate sentences are removed. FastText LangID is used before identifying bitext with LASER’s distance-based mining approach. The margin threshold is optimized by training and evaluating downstream MT models on four WMT benchmarks (de-en, de-fr, cs-de, cs-fr). The final dataset is used to train translation models that are then evaluated by automatically measuring the quality of their translations against human translations of TED talks in 45 languages, with highest quality for translations between English and, for example, pt, es, da, and lowest for sr, ja, mr, zh_TW. In the audit we focus on language pairs with English on one side.

None of the above datasets has been evaluated for quality on the sentence level (exception: several languages in ParaCrawl v3), and downstream evaluations are centered around a small fraction of higher-resource languages. This is insufficient for drawing conclusions about the quality of individual or aligned sentences, and about the entirety of languages. In addition, there might be a publication bias preventing negative results with any of the above corpora with lower quality being published.

To close this gap, we conduct a human data quality audit focused on the lowest-resource and most under-evaluated languages, but also covering mid- and high-resource languages for comparison.

### 4.1 Auditing Process

##### Participants

We recruited 51 volunteers from the NLP community, covering about 70 languages with proficient language skills.7 Each sentence is annotated by one rater. To verify our hypothesis that those annotations can largely done by non-native speakers, we repeat a set of language expert annotations by a non-expert, and measure the accuracy of the non-expert.

##### Sample Selection

For each language in each dataset, we took a random sample of 100 lines, which may be anywhere from single words to short paragraphs depending on segmentation. We manually annotated them according to the error taxonomy described below. For WikiMatrix and CCAligned, we selected those languages that are paired with English, and for ParaCrawl, we also included those paired with Spanish (“total” counts in Table 3). We did not annotate all languages, but focused on the ones with the least number of sentences in each dataset (at least the smallest 10) and languages for which we found proficient speakers. Since we annotate the same maximum number of sentences8 across all chosen languages regardless of their total number of sentences, the annotated samples are not an unbiased sample from the whole dataset.

##### Non-expert Labeling Strategies

Although many of the volunteers were familiar with the languages in question or spoke related languages, in cases where no speaker of a relevant language could be found, volunteers used dictionaries and Internet search to form educated guesses. We discuss this deeper in Appendix C to highlight how much of this low-resource focused evaluation can actually be done by non-proficient speakers with relatively low effort. In general, we aim to find an upper bound on quality, so we encouraged annotators to be forgiving of translation mistakes when the overall meaning of the sentence or large parts thereof are conveyed, or when most of the sentence is in the correct language.

##### Effort

The individual effort was dependent on the quality and complexity of the data, and on the annotator’s knowledge of the language(s), for example, it took from less than two minutes for an English native speaker to pass through 100 well-formed English sentences (or similarly to annotate languages with 0% in-language sentences), to two hours of “detective work” for well-formed content in languages for an annotator without familiarity.

##### Taxonomy

In order to quantify errors, we developed a simple error taxonomy. Sentences and sentence pairs were annotated according to a simple rubric with error classes of Incorrect Translation (X, excluded for monolingual data), Wrong Language (WL), and Non-Linguistic Content (NL). Of correct sentences (C), we further mark single words or phrases (CS) and boilerplate contents (CB). In addition, we asked annotators to flag offensive or pornographic content. Table 2 provides examples for parallel data, and Appendix B contains detailed annotation instructions.

Table 2:

Annotation codes for parallel data with sentence pair examples. The language code before each sentence indicates the language it is supposed to be in.

Correct Codes
C: Correct translation, any Combined label for CC, CB, CS

CC:Correct translation, natural sentence
en The Constitution of South Africa nso Molaotheo wa Rephabliki ya Afrika Borwa
en Transforming your swimming pool into a pond de Umbau Ihres Swimmingpools zum Teich

CB:Correct translation, Boilerplate or low quality
en Reference number: 13634 ln Motango ya référence: 13634
en Latest Smell Stop Articles fil Pinakabagong mga Artikulo Smell Stop

CS:Correct translation, Short
en movies, dad it cinema, papà
en Halloween - without me ay Hallowen – janiw nayampejj

Error Codes
X:Incorrect translation, but both correct languages
en A map of the arrondissements of Paris kg Paris kele mbanza ya kimfumu ya Fwalansa.
en Ask a question tr Soru sor Kullanıma göre seçim

WL:Source OR target wrong language, but both still linguistic content
en The ISO3 language code is zho zza Táim eadra bracach mar bhionns na frogannaidhe.
en Der Werwolf—sprach der gute Mann, de des Weswolfs, Genitiv sodann,

NL:Not a language: at least one of source and target are not linguistic content
en EntryScan 4 _ tn TSA PM704 _
en organic peanut butter ckb ♦♦♦♦♦♦♦
Correct Codes
C: Correct translation, any Combined label for CC, CB, CS

CC:Correct translation, natural sentence
en The Constitution of South Africa nso Molaotheo wa Rephabliki ya Afrika Borwa
en Transforming your swimming pool into a pond de Umbau Ihres Swimmingpools zum Teich

CB:Correct translation, Boilerplate or low quality
en Reference number: 13634 ln Motango ya référence: 13634
en Latest Smell Stop Articles fil Pinakabagong mga Artikulo Smell Stop

CS:Correct translation, Short
en movies, dad it cinema, papà
en Halloween - without me ay Hallowen – janiw nayampejj

Error Codes
X:Incorrect translation, but both correct languages
en A map of the arrondissements of Paris kg Paris kele mbanza ya kimfumu ya Fwalansa.
en Ask a question tr Soru sor Kullanıma göre seçim

WL:Source OR target wrong language, but both still linguistic content
en The ISO3 language code is zho zza Táim eadra bracach mar bhionns na frogannaidhe.
en Der Werwolf—sprach der gute Mann, de des Weswolfs, Genitiv sodann,

NL:Not a language: at least one of source and target are not linguistic content
en EntryScan 4 _ tn TSA PM704 _
en organic peanut butter ckb ♦♦♦♦♦♦♦

### 4.2 Human Audit Results

##### Interpretation of Results

For each language, we compute the percentage of each label within the 100 audited sentences. Then, we either aggregate the labels across languages with equal weights (macro-average), or weight them according to their presence in the overall dataset (micro-average). Results are shown in Table 3. The statistics for the correct codes (CC, CB, CS) are combined as C. The number of languages, the numbers of sentences per language, and the choice of languages differ across datasets, both in the original release and in the selection for our audit, so the comparison of numbers across datasets has to be taken with a grain of salt. Since the numbers are based on a small sample of sentences that were partially annotated by non-experts, the error statistics are only rough estimates. Our audit captures a decent ratio of languages (25–55%, 2nd row in Table 3), but only a tiny fraction of the overall number of sentences (0.00004–0.002%). When we speak of “low-” and “high”-resource languages, we mean languages with smaller or larger representation in the datasets at hand. When reporting language-specific results we use the original language identifiers of the datasets.

Table 3:

Averages of sentence-level annotations across datasets and selected languages. Macro-avg: Each language is weighted equally in the aggregation, regardless of its size. Micro-avg: Each label is weighted by the fraction of sentences for that language in the overall annotated corpus, i.e., the annotations for higher-represented languages are upweighted, and annotations for lower-represented languages are downweighted. The bottom rows contain the number of languages that have 0% labeled C, etc. Note that these are not true expectations since the languages audited were not randomly sampled.

ParallelMonolingual
CCAlignedParaCrawl v7.1WikiMatrixOSCARmC4
#langs audited / total 65 / 119 21 / 38 20 / 78 51 / 166 48 / 108
%langs audited 54.62% 55.26% 25.64% 30.72% 44.44%
#sents audited / total 8037 / 907M 2214 / 521M 1997 / 95M 3517 / 8.4B 5314 / 8.5B
%sents audited 0.00089% 0.00043% 0.00211% 0.00004% 0.00006%

macro C 29.25% 76.14% 23.74% 87.21% 72.40%
X 29.46% 19.17% 68.18% – –
WL 9.44% 3.43% 6.08% 6.26% 15.98%
NL 31.42% 1.13% 1.60% 6.54% 11.40%
offensive 0.01% 0.00% 0.00% 0.14% 0.06%
porn 5.30% 0.63% 0.00% 0.48% 0.36%

micro C 53.52% 83.00% 50.58% 98.72% 92.66%
X 32.25% 15.27% 47.10% – –
WL 3.60% 1.04% 1.35% 0.52% 2.33%
NL 10.53% 0.69% 0.94% 0.75% 5.01%
offensive 0.00% 0.00% 0.00% 0.18% 0.03%
porn 2.86% 0.33% 0.00% 1.63% 0.08%

#langs =0% C
#langs <50% C 44 19 11
#langs >50% NL 13
#langs >50% WL
ParallelMonolingual
CCAlignedParaCrawl v7.1WikiMatrixOSCARmC4
#langs audited / total 65 / 119 21 / 38 20 / 78 51 / 166 48 / 108
%langs audited 54.62% 55.26% 25.64% 30.72% 44.44%
#sents audited / total 8037 / 907M 2214 / 521M 1997 / 95M 3517 / 8.4B 5314 / 8.5B
%sents audited 0.00089% 0.00043% 0.00211% 0.00004% 0.00006%

macro C 29.25% 76.14% 23.74% 87.21% 72.40%
X 29.46% 19.17% 68.18% – –
WL 9.44% 3.43% 6.08% 6.26% 15.98%
NL 31.42% 1.13% 1.60% 6.54% 11.40%
offensive 0.01% 0.00% 0.00% 0.14% 0.06%
porn 5.30% 0.63% 0.00% 0.48% 0.36%

micro C 53.52% 83.00% 50.58% 98.72% 92.66%
X 32.25% 15.27% 47.10% – –
WL 3.60% 1.04% 1.35% 0.52% 2.33%
NL 10.53% 0.69% 0.94% 0.75% 5.01%
offensive 0.00% 0.00% 0.00% 0.18% 0.03%
porn 2.86% 0.33% 0.00% 1.63% 0.08%

#langs =0% C
#langs <50% C 44 19 11
#langs >50% NL 13
#langs >50% WL
##### Which Datasets Have Quality Issues?

The macro-averaged results show that the ratio of correct samples (C) ranges from 24% to 87%, with a large variance across the five audited datasets. Particularly severe problems were found in CCAligned and WikiMatrix, with 44 of the 65 languages that we audited for CCAligned containing under 50% correct sentences, and 19 of the 20 in WikiMatrix. In total, 15 of the 205 language-specific samples (7.3%) contained not a single correct sentence. For the parallel datasets we are also interested in the quantity of misaligned/mistranslated sentences (X). For WikiMatrix, two-thirds of the audited samples were on average misaligned. We noticed that sentences were often similar in structure, but described different facts (see Table 6). This might originate from the nature of the underlying Wikipedia articles, since they are often comparable rather than parallel (Schwenk et al., 2021).

Figure 1 illustrates per-corpus correctness more completely, showing for each dataset what percent of audited corpora are under each possible threshold of correctness.

Figure 1:

Fraction of languages in each dataset below a given quality threshold (percent correct).

Figure 1:

Fraction of languages in each dataset below a given quality threshold (percent correct).

Close modal
##### Why Haven’t These Problems Been Reported Before?

The findings above are averaged on a per-language basis (i.e., macro-average), and therefore give low and high-resource languages equal weight. If we instead estimate the quality on a per-sentence basis (i.e., down-weight lower-resource languages in the computation of the average), the numbers paint a more optimistic picture (“micro” block in Table 3). This is especially relevant for the monolingual datasets because they contain audits for English, which makes up for 43% of all sentences in OSCAR and 36% in mC4. To illustrate the effect of this imbalance: A random sample from the entire mC4 dataset with over 63% chance will be from one of the 8 largest languages (en, ru, es, de, fr, it, pt, pl, >100M sentences each), of which all have near perfect quality. Analogously, evaluation and tuning of web mining pipelines and resulting corpora in downstream applications focused largely on higher-resource languages (Section 3), so the low quality of underrepresented languages might go unnoticed if there is no dedicated evaluation, or no proficient speakers are involved in the curation (Nekoto et al., 2020).

##### How Much Content is Nonlinguistic or in the Wrong Language?

Nonlinguistic content is a more common problem than wrong-language content. Among the parallel datasets, CCAligned contains the highest percentage of nonlinguistic content, at 31.42% on average across all rated corpora, and also the highest percent of wrong-language content, at 9.44%. Among the monolingual datasets, mC4 contains the highest ratio both of sentences in incorrect languages (15.98% average) and nonlinguistic content (11.40% average), with 4 of the 48 audited languages having more than 50% contents in other languages. The low amount of wrong language in ParaCrawl shows the benefits of selecting domains by the amount in-language text, but the dataset also covers the smallest amount of languages. The low ratio of wrong language samples in OSCAR may reflect the success of line-level LangID filtering. These numbers provide evidence that more research in LangID could improve the overall quality, especially with respect to nonlinguistic content.

##### Which Languages Got Confused?

The languages that were confused were frequently related higher-resource languages. However, there were also a significant number of “out-of-model cousin” cases, where languages not supported by the LangID model ended up in a similar-seeming language. For instance in mC4, much of the Shona (sn, Bantu language spoken in Zimbabwe and Mozambique) corpus is actually Kinyarwanda (rw, Bantu language spoken in mostly in Rwanda and Uganda)—and, peculiarly, much of the Hawaiian (haw, Polynesian language spoken in Hawaii) is actually Twi (tw/ak, Central Tano language spoken mostly in Ghana).

##### Do Low-resource Languages Have Lower Quality?

Low-resource datasets tend to have lower human-judged quality. The Spearman rank correlation between quality (%C) and size is positive in all cases. The trend is strongest for mC4 (r = 0.66), and gradually declines for CCAligned (r = 0.53), WikiMatrix (r = 0.49), ParaCrawl (r = 0.43), and OSCAR (r = 0.37). Figure 2 compares the number of sentences for each language against the proportion of correct sentences: Not all higher-resource languages ( >106 sentences) have high quality, in particular for CCAligned (e.g., Javanese (en--jv_ID) with 5%C, or Tagalog (en--tl_XX) with 13%C). For mid-resource languages (104–106 sentences) the picture is inconclusive, with some languages having high quality, and others having extremely low quality, even within the same datasets (e.g., Urdu in CCAligned en-ur_PK has 100%C vs. its romanized counterpart en--ur_PK_rom 0.5% C). For individual error codes trends are less clear (not depicted).

Figure 2:

Percentage of sentences labeled as correct vs. log N sentences for all audited languages.

Figure 2:

Percentage of sentences labeled as correct vs. log N sentences for all audited languages.

Close modal
##### Which Languages Have the Lowest Quality?

Across datasets we observe that the quality is particularly poor for languages that are included in romanized script (_rom/_latn), but are more commonly written in other scripts (e.g., Urdu (ur), Japanese (ja), Arabic (ar)). These are not transliterations of other scripts, but mostly contain non-linguistic material or wrong languages (e.g., the romanized Japanese corpus in mC4 (ja_latn) contains Spanish, French, English, Portuguese, among others). In terms of geography, the poorest quality is found for African languages (Bambara (bm), Fula (ff), Kikongo (kg), Luganda (lg), Lingala (ln), Norther Sotho (nso), Oromo (om), Shona (sn), Somali (so), Tswana (tn), Wolof (wo)), minority languages in Europe and the Middle East that are closely related to higher-resource languages (Azerbaijani (az-IR), North Frisian (frr), Neapolitan (nap), Silesian (szl), Zaza (zza)), lesser spoken Chinese languages sharing a script with Mandarin (Yue (yue), Wu (wuu)), four major Austronesian (Central Bikol (bcl), Chavacano (cbk), Javanese (jv), Sundanese (su)), and some South-Asian languages, in particular Sinhala (si). Appendix D contains the detailed per-language statistics for all corpora.

##### What Is the Incidence of Offensive and Pornographic Content?

Overall, the sampled sentences did not contain a large amount of offensive content. However, there were notable amounts of pornographic content ( >10%) found in CCAligned for 11 languages.

##### Annotation Quality

For a subset of audited languages from CCAligned and OSCAR we measure the accuracy (Acc) of the labels assigned by non-proficient speakers against the labels assigned by proficient speakers for all audited sentences. This can be understood as a directed measure of annotator agreement for the special case where one rater is an expert and the other is not. Results for varying label granularity are reported in Tables 4 and 5. For n = 6 all classes of the taxonomy were distinguished, for n = 4 the C subclasses were combined, and for n = 2 it is binary decision between C and the rest of the error classes. With the full 6-class taxonomy (Acc-6) we find a mean accuracy of 0.66 for CCAligned audits, and 0.98 for OSCAR audits. With a binary taxonomy (Acc-2) distinguishing C from the rest, the accuracy further increases to 0.79 for CCAligned. This provides strong evidence that good quality annotations are not limited to those proficient in a language.

Table 4:

Rater evaluation for a subset of audits from CCAligned (translated from English) measured by the accuracy (Acc-n) of annotations by non-proficient speaker against annotations by proficient speakers.

es_XXbm_MLyo_NGtr_TRku_TRzh_CNaf_ZAjv_IDzh_TWit_ITmean
Acc-6 0.58 0.73 0.41 0.45 0.43 0.55 0.65 0.55 0.46 0.55 0.66
Acc-4 0.77 0.73 0.60 0.55 0.56 0.72 0.72 0.57 0.58 0.66 0.72
Acc-2 0.91 0.96 0.72 0.64 0.71 0.79 0.77 0.92 0.81 0.69 0.79
es_XXbm_MLyo_NGtr_TRku_TRzh_CNaf_ZAjv_IDzh_TWit_ITmean
Acc-6 0.58 0.73 0.41 0.45 0.43 0.55 0.65 0.55 0.46 0.55 0.66
Acc-4 0.77 0.73 0.60 0.55 0.56 0.72 0.72 0.57 0.58 0.66 0.72
Acc-2 0.91 0.96 0.72 0.64 0.71 0.79 0.77 0.92 0.81 0.69 0.79
Table 5:

Rater evaluation for a subset of audits from OSCAR measured by the accuracy (Acc-n) of annotations by non-proficient speaker against annotations by proficient speakers.

tyvrmbaremlzhlamean
Acc-6 1.0 0.98 1.0 1.0 0.86 1.0 0.98
Acc-4 1.0 1.0 1.0 1.0 0.87 1.0 0.98
Acc-2 1.0 1.0 1.0 1.0 0.87 1.0 0.98
tyvrmbaremlzhlamean
Acc-6 1.0 0.98 1.0 1.0 0.86 1.0 0.98
Acc-4 1.0 1.0 1.0 1.0 0.87 1.0 0.98
Acc-2 1.0 1.0 1.0 1.0 0.87 1.0 0.98

However, the significant drop of accuracy for finer-grained labels hints at that our taxonomy can be further improved, especially for parallel sentences. The error taxonomy lacks at least one category of error, namely, “correct/in-language but unnatural”. Similarly, the definition of “correct-short” and “correct-boilerplate” were not understood equally by all annotators and the concept of “correct-short” has potential issues for agglutinative languages like Turkish. Finally, it was unclear what to do with related dialects, for example, when a sentence is “almost correct but wrong dialect” or when it is unclear which dialect a sentence belongs to. We recommend including these categories for future audits.

### 4.3 Automatic Filtering

Given the frequency of WL and NL annotations, it might be tempting to use open-source LangID models to post-filter data on a per-sentence(-pair) level, as OSCAR does. Unfortunately, this turns out to have its own issues.

##### Sentence-level n-gram LangID Filtering

We classify all sentence pairs of CCAligned with CLD3, an n-gram based LangID model. By comparing its predictions to the audit labels, we evaluate its quality on the subset of annotated samples: The classifier should detect both correct languages when the pair is annotated as C and X, and should detect incorrect languages in the pair when WL and NL. On this task, the CLD3 classifier achieves an average precision of only 40.6%.

##### Sentence-level Transformer LangID Filtering

n-gram LangID models like CLD3 have known problems. However, Caswell et al. (2020) demonstrate that semi-supervised Transformer-based LangID models strongly out-perform them. We train a comparable Transformer-based LangID model and apply it to our annotated CCAligned data. We find that filtering noisy corpora ( < 50% correct) on LangID for both source and target leads to gains in median precision, rising from 13.8% pre-filter to 43.9% post-filter. However, this comes at a steep cost of 77.5% loss in recall. The biggest winners were Lingala, whose precision climbs from 8% to 80%, and Oromo, which soars from 2% to 33% in-language. Both of these, however, come at the cost of losing 50% of the correct in-language sentences, being reduced from 22k sentences to 3k and 1k sentences, respectively, which would likely be too small for building downstream models. The moral is that, at least at the current stage, there is no one-size-fits-all approach for sentence-level LangID filtering.

Standardized and unambiguous representations of language codes are important for practical data use and exchange. The standard used by most academic and industry applications is BCP-47 (Phillips and Davis, 2005), which builds off the two-letter ISO639-2 codes and three-letter ISO639-3 codes, but also allows for adding subtags for scripts (e.g., Hindi in Latin script: hi-Latn) or regional varieties (e.g., French spoken in Canada: fr-CA). It would enhance transparency and interoperability if adopted consistently, especially with growing language diversity in NLP.

We find a variety of errors and inconsistencies in language code usage, ranging from serious mislabelings to small transgressions against standard conventions. For this analysis, we also include the JW300 (Agić and Vulić, 2019) dataset, a multilingual dataset crawled from jw.org. In summary, we find 8 nonstandard codes in CCAligned, 3 in OSCAR, 1 in mC4, 1 in WikiMatrix, and 70 in JW300, for 83 in total. This does not include the 59 codes affected by superset issues. Full details are given in Appendix A.

##### Inconsistent Language Codes

One common issue is simply using nonstandard or invented codes. For example, CCAligned uses only two-letter codes, so when the BCP-47 code for a language is three letters it is either shortened (e.g., zza$→$zz) or invented (shn$→$qa). Similarly, OSCAR contains data labeled as als (BCP-47 for Tosk Albanian) that is actually in gsw (Allemannic).9 Twenty-two additional language codes in JW300 have similar issues, including 12 codes that start with jw_ but are not Javanese.

##### False Sign Languages

Twelve percent (48/417) of JW300 carry language codes for sign languages. Instead of sign language transcripts they are texts in another high-resource language, mostly English or Spanish—for example, the en-zsl (Zambian sign language) data is actually English-English parallel data (copies), details in Appendix A. This was likely caused by videos with sign language interpretation embedded on the crawled Web sites.10

##### Mysterious Supersets

When datasets contain language codes that are supersets of other language codes, it is difficult to determine which particular language the text contains. WikiMatrix has Serbian (sr), Croatian (hr), Bosnian (bs), and Serbo-Croatian (sh)—their superset.11 The issue of codes that are supersets of others is common enough to include a small table dedicated to it (Appendix Table 7). In some cases this may not be an issue, as with Arabic, where ar conventionally refers to Modern Standard Arabic, even though the code technically encompasses all dialects. In many cases, the nature of the data in the superset code remains a mystery.

##### Deprecated Codes

Finally, there are several deprecated codes that are used: sh in WikiMatrix, iw in mC4, sh and eml in Oscar, and daf in JW300.

##### Low Quality in Downstream Applications

Text corpora today are building blocks for many downstream NLP applications like question answering and text summarization—for instance, a common approach is to first train translation models on such data and then automatically translate training data for downstream models (Conneau et al., 2018). If the data used for the original systems is flawed, derived technology may fail for those languages far down the line without knowing the causes. This risk of undesired downstream effects calls for future studies with a careful treatment of intertwined effects such as data size and domain, language-specific phenomena, evaluation data and metric biases. To give the reader a brief glimpse of the impact of data quality for the example of translation, we compare the C% metric from our audit with the translation quality (sentencepiece-BLEU, spBLEU) of the multilingual translation model M2M124 for 124 languages (Goyal et al., 2021). It was trained on WikiMatrix and CCAligned, and similar data collected with the same tools, which we expect to show similar biases. Translation quality is evaluated on the trusted, human-translated FloReS benchmark (Goyal et al., 2021). For the 21 languages present in both the audit and the FloReS benchmark, we found a positive correlation (Spearman) between the data quality scores and spBLEU of ρ = 0.44(p = 0.041). This is not as large as the correlation with data size (ρ = 0.66, p = 0.00078), but it nonetheless helps to explain translation quality—the correlation between the product of C% and data size (in other words, the expected total number of good sentences in the dataset), is the highest yet, with a value of ρ = 0.73(p = 0.00013).12

##### Representation Washing

Since there are datasets that contain many low-resource languages, the community may feel a sense of progress and growing equity, despite the actual quality of the resources for these languages. Similarly, if low-quality datasets are used as benchmarks they may exaggerate model performance, making low-resource NLP appear more solved than it is—or conversely, if models perform poorly when trained with such data, it may be wrongly assumed that the task of learning models for these languages is harder than it actually is or infeasible given current resources. These effects could result in productive effort being redirected away from these tasks and languages.

##### Trust in Incorrect “Facts”

We found many instances of parallel-looking sentences that are structurally and semantically similar, but not factually correct translations (Table 6). They can cause models to produce plausible “translations” that are factually wrong, but users may still trust them (algorithmic trust) without verifying the information. Similarly, automation bias (Skitka et al., 1999), referring to humans favoring decisions made by automated systems over decisions made by humans, might amplify the issues of inaccurate translations caused by misalignments.

Table 6:

Examples of “parallel” data where the translation has a different meaning than the source, but the form looks the same. (We added translations of the non-English side.) Such data may encourage hallucinations of fake “facts”.

 en The prime minister of the UK is Boris Johnson. nl De minister-president van Nederland is Mark Rutte. en: The prime minister of the Netherlands is Mark Rutte. en 24 March 2018 pt 14 Novembro 2018 en: 14 November 2018 en The current local time in Sarasota is 89 minutes. nn Den lokale tiden i Miami er 86 minutt. en: The local time in Miami is 86 minutes. en In 1932 the highway was extended north to LA. bar 1938 is de Autobahn bei Inglstod fertig gstellt. en: The highway near Inglstod was completed in 1938.
 en The prime minister of the UK is Boris Johnson. nl De minister-president van Nederland is Mark Rutte. en: The prime minister of the Netherlands is Mark Rutte. en 24 March 2018 pt 14 Novembro 2018 en: 14 November 2018 en The current local time in Sarasota is 89 minutes. nn Den lokale tiden i Miami er 86 minutt. en: The local time in Miami is 86 minutes. en In 1932 the highway was extended north to LA. bar 1938 is de Autobahn bei Inglstod fertig gstellt. en: The highway near Inglstod was completed in 1938.

Of the five multilingual corpora evaluated, we consistently found severe issues with quality, especially in the lower-resource languages. We rated samples of 205 languages, and found that 87 of them had under 50% usable data, with a full 15 languages at 0% in-language. We furthermore found consistent issues with mislabeled data and nonstandard language codes, particularly in the JW300 dataset, and identified 83 affected corpora, at least 48 of which were entirely spurious (Section 5). While there might have been anecdotal evidence of insufficient quality for some of the datasets, the majority of these quality issues had not been reported, nor been investigated in depth. These issues might go unnoticed for languages that are not represented in the evaluation of the crawling methods, and cause harm in downstream applications (Khayrallah and Koehn, 2018).

There are a variety of ways to improve both the ease and accuracy of human evaluation, as well a few classes of issues we ignored in this paper, like close dialects. Ideally we would like to build a standard suite of automatic metrics for datasets, but more research is necessary to determine what the appropriate metrics would be. One important area missing from our analyses, however, is the estimated portion of a dataset which has been generated by MT (Rarrick et al., 2011), LM systems, or bots/templates, as for example in the analysis of C4 (Dodge et al., 2021). The information captured in machine-generated content might still be useful for modeling, but might falsely overrepresent typical generation patterns and introduce linguistic errors or unnatural artifacts.

We therefore strongly recommend looking at samples of any dataset before using it or releasing it to the public. As we have shown, one does not need to be proficient in a language to see when there are serious quality issues, and a quick scan of 100 sentences can be sufficient to detect major problems. Moreover, going through and annotating a small sample of data can bring actionable insights about new ways to filter or use it.

If data quality issues are found, a wide variety of techniques can be explored, like filtering on length-ratio, LangID, TF-IDF wordlists (Caswell et al., 2020), or dictionaries (Kamholz et al., 2014); to neural approaches like LM scoring (Axelrod et al., 2011; Moore and Lewis, 2010; Wang et al., 2018). Unfortunately, none of these provides a quick and easy fix, especially for low-resource languages—data cleaning is no trivial task!

Noisy datasets are by no means useless, at least if they contain some desirable content. Therefore an alternative to filtering can be documentation (Bender et al., 2021). This can take the form of a per-language quality score and notes about known issues, a datasheet (Gebru et al., 2018) or nutrition label (Holland et al., 2018). However, we suggest researchers not release corpora with near-zero in-language content, as this may give the mistaken impression of usable resources.

Finally, we encourage the community to continue conducting evaluations and audits of public datasets—similar to system comparison papers.

We would like to thank the TACL editors and reviewers, and AfricaNLP and Google reviewers who have helped us shape this paper. Furthermore, we are grateful for Ahmed El-Kishky’s support and help with CCAligned and WikiMatrix size statistics.

Table 7 provides a complete lists of the corpora where one code is defined as a superset of the other by the ISO standard, and in Table 8 we provide a complete list of the language codes in JW300 which purport to be sign language but are actually unrelated high-resource languages.

Table 7:

Situations where two language codes are represented, but one is a superset of another by the ISO standard, leading to unclarity about the data in the supercode dataset. *The als dataset is actually in gsw.

DatasetSupercodeSubcode(s)
JW300 kg kwy
JW300 mg tdx
JW300 qu que, qug, qus
quw, quy, quz
qvi, qvz

JW300 sw swc

OSCAR ar arz
OSCAR az azb
OSCAR sh bs, hr, sr
OSCAR ku ckb
OSCAR ms id, min
OSCAR no nn
OSCAR sq als*
OSCAR zh yue, wuu

WikiMatrix ar arz
WikiMatrix sh bs, hr, sr
WikiMatrix zh wuu
DatasetSupercodeSubcode(s)
JW300 kg kwy
JW300 mg tdx
JW300 qu que, qug, qus
quw, quy, quz
qvi, qvz

JW300 sw swc

OSCAR ar arz
OSCAR az azb
OSCAR sh bs, hr, sr
OSCAR ku ckb
OSCAR ms id, min
OSCAR no nn
OSCAR sq als*
OSCAR zh yue, wuu

WikiMatrix ar arz
WikiMatrix sh bs, hr, sr
WikiMatrix zh wuu
Table 8:

There are 48 languages in the JW300 corpus with language codes that correspond to sign languages, but in reality are unrelated high-resource languages (usually the most spoken language in the country of origin of the sign language). This table shows the actual language of the data corresponding to each sign language code.

Actual languageCode in JW300
cs cse
de gsg
el gss
en ase, asf, bfi, ins, psp, sfs, zib, zsl
es aed, bvl, csf, csg, csn, csr, ecs, esn, gsm, hds, lsp, mfs, ncs, prl, pys, ssp, vsl
fi fse
fr fcs,fsl
hu hsh
id inl
it ise
ja jsl
ko kvk
pl pso
pt bzs, mzy, psr, sgn_AO
ro rms
ru rsl
sk svk
sq sql
st jw_ssa
zh csl, tss
Actual languageCode in JW300
cs cse
de gsg
el gss
en ase, asf, bfi, ins, psp, sfs, zib, zsl
es aed, bvl, csf, csg, csn, csr, ecs, esn, gsm, hds, lsp, mfs, ncs, prl, pys, ssp, vsl
fi fse
fr fcs,fsl
hu hsh
id inl
it ise
ja jsl
ko kvk
pl pso
pt bzs, mzy, psr, sgn_AO
ro rms
ru rsl
sk svk
sq sql
st jw_ssa
zh csl, tss

Special attention needs to be given to the JW300 dataset, which, in addition to the sign languages and superset code issues, has a variety of other peculiarities. These problems seem to originate in the codes used by jw.org,13 which were apparently not checked in the creation of the JW300 dataset. An overview is provided in Table 9, and the following paragraphs give specifics.

Table 9:

Language code issues in the JW300 datasets for 22 language varieties not covered by Tables 7 and 8. Private use extensions are given as they appear in jw.org, and specified as ‘?’ if they are absent from jw.org.

Code in JW300BCP-47 codeActual Language Name
Incorrect private-use extensions
hy_arevmda hyw Western Armenian
jw_dgr os_x_dgr Digor Ossetian
jw_dmr naq_x_dmr Damara Khoekhoe
jw_ibi yom_x_ibi Ibinda Kongo
jw_paa pap_x_paa Papiamento (Aruba)
jw_qcs qxl Salasaca Highland Kichwa
jw_rmg rmn_x_rmg Greek Romani (South)
jw_rmv rmy_x_rmv Vlax Romani, Russia
jw_spl nso_x_spl Sepulana
jw_ssa st_ZA Sesotho (South Africa)
jw_tpo pt_PT Portuguese (Portugal)
jw_vlc ca_x_vlc Catalan (Valencia)
jw_vz skg_x_vz Vezo Malagasy
rmy_AR rmy_x_? Kalderash

Equivalent codes used in place of extensions
kmr_latn kmr_x_rdu Kurmanji (Caucasus)
nya ny_x_? Chinyanja (Zambia)
que qu_x_? Quechua (Ancash)

Deprecated codes
daf dnj/lda Dan

ISO-693-3 used in place of ISO-693-2
cat ca Catalan
gug gn Guarani
run rn Kirundi
tso_MZ ts_MZ Changana (Mozambique)
Code in JW300BCP-47 codeActual Language Name
Incorrect private-use extensions
hy_arevmda hyw Western Armenian
jw_dgr os_x_dgr Digor Ossetian
jw_dmr naq_x_dmr Damara Khoekhoe
jw_ibi yom_x_ibi Ibinda Kongo
jw_paa pap_x_paa Papiamento (Aruba)
jw_qcs qxl Salasaca Highland Kichwa
jw_rmg rmn_x_rmg Greek Romani (South)
jw_rmv rmy_x_rmv Vlax Romani, Russia
jw_spl nso_x_spl Sepulana
jw_ssa st_ZA Sesotho (South Africa)
jw_tpo pt_PT Portuguese (Portugal)
jw_vlc ca_x_vlc Catalan (Valencia)
jw_vz skg_x_vz Vezo Malagasy
rmy_AR rmy_x_? Kalderash

Equivalent codes used in place of extensions
kmr_latn kmr_x_rdu Kurmanji (Caucasus)
nya ny_x_? Chinyanja (Zambia)
que qu_x_? Quechua (Ancash)

Deprecated codes
daf dnj/lda Dan

ISO-693-3 used in place of ISO-693-2
cat ca Catalan
gug gn Guarani
run rn Kirundi
tso_MZ ts_MZ Changana (Mozambique)

Twelve languages in JW300 have codes starting in jw_, suggesting they are varieties of Javanese (ISO639-1 jw), but are instead attempts to represent language dialects for which there are no BCP-47 codes. These codes seem to have been updated in jw.org to appropriate BCP-47 private-use extensions in the form <supercode>_x_<tag>, which are provided in Table 9. Twelve languages have codes starting in jw_, suggesting they are varieties of Javanese, but are instead mis-parsed private-use extensions. Three codes appear in addition to equivalent ISO codes, making it unclear which languages they are. One language uses a deprecated ISO code. Four languages use the ISO639-3 code instead of the ISO639-2 code, and therefore are not BCP-47.

In addition to the jw_ tags, there are two other mis-used private subtags: hy_arevmda, which in addition to lacking the mandatory _x_ appears to represent standard Western Armenian (hyw); and rmy_AR, which, rather than being Romany from Argentina, is Kalderash Romany.

There are also a few anomalies where private use extensions should have been used but other methods were found to convey the distinctions. Three codes appear in addition to equivalent ISO codes, making it unclear which languages they are. Two of these are equivalencies between ISO639-2 and ISO639-3 (nya and ny are both Chichewa, qu and que are both Quechua), and one is a script equivalency (kmr and kmr_latn are both in Latin script). In these three cases the two codes do represent different languages—so a private use extension would have been appropriate.

Finally, there is the more minor issue that three languages use the ISO639-3 code instead of the ISO639-2 code, and therefore are not BCP-47.

In addition to the JW300-specific errors, Table 10 summarizes miscellaneous errors in CCAligned and OSCAR that were detailed in Section 5.

Table 10:

Miscellaneous errors in language codes.

DatasetCode in CorpusCorrect Code
CCAligned zz zza
CCAligned sz szl
CCAligned ns nso
CCAligned cb ckb
CCAligned tz ber
CCAligned qa shn
CCAligned qd kac
CCAligned cx ceb

mC4 iw he

OSCAR eml egl
OSCAR als gsw
OSCAR sh hbs

WikiMatrix sh hbs
DatasetCode in CorpusCorrect Code
CCAligned zz zza
CCAligned sz szl
CCAligned ns nso
CCAligned cb ckb
CCAligned tz ber
CCAligned qa shn
CCAligned qd kac
CCAligned cx ceb

mC4 iw he

OSCAR eml egl
OSCAR als gsw
OSCAR sh hbs

WikiMatrix sh hbs

In addition to the examples given in Table 2, raters were provided with the following verbal notes on the error codes:

• CC: Correct translation, natural sentence: It’s OK if it’s a sentence fragment instead of a whole sentence, as long as it is not too short (about 5 words or greater). The translation does not have to be perfect.

• CS: Correct translation, but single word or short phrase: Also includes highly repeated short phrases, like “the cat the cat the cat the cat the cat ...”

• CB: Correct translation, but boilerplate: This can be auto-generated or formulaic content, or content that one deems “technically correct but generally not very useful to NLP models”. Unfortunately, it’s often not clear what should be counted as boilerplate...do your best.

• X: Incorrect translation [for parallel sentences] both source and target are in the correct language, but they are not adequate translations.

• WL: Wrong language For short sentences, especially with proper nouns, there is often a fine line between “Wrong language” and “Not language”. Do your best.

• NL: Not language At least one of source and target are not linguistic content. Any sentence consisting only of a proper noun (e.g. “Tyrone Ping”) should be marked as NL.

• U: Unknown for sentences that need verification by a native speaker. This is an auxiliary label that is resolved in most cases.

A surprising amount of work can be done without being an expert in the languages involved. The easiest approach is simply to search the internet for the sentence, which usually results in finding the exact page the sentence came from, which in turn frequently contains clues like language codes in the URL, or a headline like News in X language, sometimes with references to a translated version of the same page. However, for the cases where this is insufficient, here are a few tips, tricks, and observations.

###### No Skills Required:

Things that do not require knowledge of the language(s) in question.

1. “Not language” can usually be identified by anyone who can read the script, though there are tricky cases with proper nouns.

2. Frequently, “parallel” sentences contain different numbers in the source and target (especially autogenerated content), and are easy to disqualify.

3. Errors tend to repeat. If a word is mistranslated once, it will often be mistranslated many more times throughout a corpus, making it easy to spot.

###### Basic Research Required:

Things that do not require knowledge of the language(s) in question but can be done with basic research.

1. If it’s written in the wrong script it’s considered wrong language. (Sometimes the writing system is indicated in the published corpus, e.g., bg-Latn, but usually the language has a “default” script defined by ISO.)

2. Some types of texts come with inherent labels or markers, such as enumerators or verse numbers.

3. When all else fails, search the internet for the whole sentence or n-grams thereof! If the whole sentence can be found, frequently the language is betrayed by the web page (the language’s autonym is useful in this case).

Tables 11, 12, 13, 14, and 15 give the complete annotation percentages for CCAligned, WikiMatrix, ParaCrawl, mC4 and OSCAR, respectively. For each annotation label, we report the ratio of the annotated sentences (of max 100 sentences) that were assigned that label by the primary annotator. Repeated annotations done for agreement measurement are not included. The C column aggregates all correct sub-codes (CC, CS, CB). We also report the total number of sentences that each dataset contains for each language and the average sentence length for the audited sentences to illustrate differences across languages. The original language codes as they are published with the datasets are maintained for the sake of consistency (but should be handled with care in future work, see Section 5), and those with less than 20% correct sentences are highlighted.

Table 11:

Audit results for a sample of 100 sentences from CCAligned for each language pair, compared to the number of sentences available in the dataset. If fewer than 100 sentences were available, all sentences were audited. Language codes are as originally published. The length is measured in number of characters and averaged across the audited portion of each corpus. Languages with less than 20% correct sentences are boldfaced.

CCCCSCBXWLNLporn#sentencesavg target length
en-sz_PL 0.00% 0.00% 0.00% 0.00% 0.00% 8.33% 91.67% 0.00% 12 71.42
en-mt_MT 3.85% 0.00% 3.85% 0.00% 50.00% 26.92% 19.23% 0.00% 26 12.58
en-tz_MA 12.12% 6.06% 6.06% 0.00% 45.45% 36.36% 6.06% 0.00% 33 57.33
en-zz_TR 0.00% 0.00% 0.00% 0.00% 8.82% 61.76% 29.41% 0.00% 34 46.53
en-kg_AO 1.35% 0.00% 1.35% 0.00% 14.86% 2.70% 81.08% 0.00% 74 29.20
en-qa_MM 11.03% 5.88% 3.68% 1.47% 72.06% 3.68% 13.24% 0.00% 136 55.28
en-bm_ML 6.04% 4.03% 2.01% 0.00% 26.85% 6.71% 60.40% 0.00% 149 32.19
en-az_IR 6.93% 6.93% 0.00% 0.00% 20.79% 13.86% 58.42% 0.00% 158 115.85
en-qd_MM 7.92% 4.95% 1.98% 0.99% 81.19% 3.96% 6.93% 0.00% 179 60.34
en-ay_BO 51.00% 33.00% 18.00% 0.00% 29.00% 3.00% 17.00% 0.00% 475 92.19
en-ak_GH 14.23% 13.60% 0.63% 0.00% 46.86% 19.25% 19.67% 0.00% 478 45.85
en-st_ZA 48.57% 42.14% 0.00% 6.43% 40.71% 1.43% 9.29% 0.00% 904 111.83
en-ve_ZA 60.40% 29.70% 21.78% 8.91% 28.71% 3.96% 6.93% 0.00% 1555 82.99
en-ts_ZA 51.49% 34.65% 11.88% 4.95% 40.59% 2.97% 4.95% 0.00% 1967 73.93
en-or_IN 42.61% 6.09% 24.35% 12.17% 38.26% 9.57% 9.57% 0.00% 5526 71.39
en-ns_ZA 4.00% 2.00% 0.00% 2.00% 23.00% 15.00% 58.00% 4.00% 14138 33.52
en-lg_UG 6.00% 0.00% 6.00% 0.00% 68.00% 17.00% 9.00% 2.00% 14701 15.83
en-ln_CD 8.00% 4.00% 3.00% 1.00% 14.00% 4.00% 74.00% 4.00% 21562 28.80
en-om_KE 2.00% 2.00% 0.00% 0.00% 31.00% 38.00% 29.00% 24.00% 22206 23.83
en-ss_SZ 12.65% 9.04% 3.61% 0.00% 13.25% 24.10% 50.00% 13.86% 22960 25.30
en-te_IN_rom 0.00% 0.00% 0.00% 0.00% 25.00% 8.00% 67.00% 5.00% 25272 24.21
en-cb_IQ 4.00% 1.00% 3.00% 0.00% 30.00% 18.00% 48.00% 11.00% 52297 30.04
en-tn_BW 0.00% 0.00% 0.00% 0.00% 6.90% 8.97% 63.45% 10.34% 71253 16.80
en-ff_NG 0.00% 0.00% 0.00% 0.00% 0.00% 8.00% 92.00% 2.00% 73022 33.59
en-sn_ZW 5.00% 1.00% 3.00% 1.00% 81.00% 14.00% 0.00% 0.00% 86868 102.59
en-wo_SN 0.00% 0.00% 0.00% 0.00% 1.71% 3.31% 94.98% 18.46% 88441 27.25
en-br_FR 17.00% 3.00% 1.00% 13.00% 37.00% 14.00% 32.00% 1.00% 115128 41.68
en-zu_ZA 55.00% 39.00% 3.00% 13.00% 30.00% 7.00% 8.00% 3.00% 126101 79.32
en-ku_TR 36.52% 12.17% 13.04% 11.30% 33.04% 28.70% 1.74% 1.74% 137874 90.51
en-ig_NG 58.00% 49.00% 3.00% 6.00% 29.00% 12.00% 1.00% 0.00% 148146 83.42
en-kn_IN 46.00% 9.00% 6.00% 31.00% 46.00% 2.00% 5.00% 4.00% 163921 70.20
en-yo_NG 34.93% 6.16% 10.96% 17.81% 34.93% 12.33% 17.81% 0.00% 175192 75.01
en-ky_KG 44.12% 24.51% 17.65% 1.96% 33.33% 22.55% 0.00% 0.98% 240657 69.56
en-tg_TJ 46.08% 18.63% 24.51% 2.94% 32.35% 20.59% 0.98% 4.90% 251865 75.31
en-ha_NG 30.00% 25.00% 3.00% 2.00% 49.00% 9.00% 12.00% 1.00% 339176 60.78
en-am_ET 59.11% 35.47% 2.46% 21.18% 37.44% 2.96% 0.49% 0.00% 346517 58.29
en-km_KH 56.12% 12.24% 33.67% 10.20% 42.86% 1.02% 0.00% 0.00% 412381 71.35
en-ne_NP 47.00% 10.00% 13.00% 24.00% 15.00% 8.00% 30.00% 14.00% 487155 79.14
en-su_ID 35.00% 15.00% 15.00% 5.00% 13.00% 13.00% 39.00% 0.00% 494142 57.08
en-ur_PK_rom 0.50% 0.00% 0.50% 0.00% 18.91% 27.36% 53.23% 5.47% 513123 18.41
en-ht_HT 55.67% 8.25% 10.31% 37.11% 35.05% 6.19% 3.09% 1.03% 558167 101.95
en-mn_MN 33.00% 8.00% 14.00% 11.00% 42.00% 7.00% 18.00% 12.00% 566885 44.43
en-te_IN 69.00% 42.00% 11.00% 16.00% 27.00% 1.00% 3.00% 1.00% 581651 97.95
en-kk_KZ 68.32% 40.59% 18.81% 8.91% 18.81% 8.91% 3.96% 1.98% 689651 72.36
en-be_BY 90.00% 57.00% 13.00% 20.00% 10.00% 0.00% 0.00% 2.00% 1125772 118.45
en-af_ZA 63.00% 40.00% 23.00% 0.00% 31.00% 2.00% 4.00% 12.00% 1504061 105.45
en-jv_ID 5.05% 1.01% 1.01% 3.03% 25.25% 10.10% 59.60% 8.08% 1513974 18.34
en-hi_IN_rom 1.00% 0.00% 0.00% 1.00% 39.00% 21.00% 39.00% 8.00% 3789571 18.13
en-lv_LV 59.00% 37.00% 9.00% 13.00% 31.00% 7.00% 3.00% 14.00% 4850957 83.67
en-ar_AR_rom 0.00% 0.00% 0.00% 0.00% 0.00% 4.00% 96.00% 4.00% 5584724 16.69
en-tl_XX 13.00% 6.00% 3.00% 4.00% 24.00% 26.00% 37.00% 5.00% 6593250 37.03
en-uk_UA 63.00% 42.00% 8.00% 13.00% 35.00% 1.00% 1.00% 5.00% 8547348 67.88
en-zh_TW 46.00% 11.00% 31.00% 4.00% 47.00% 6.00% 1.00% 1.00% 8778971 24.89
en-el_GR 49.00% 15.00% 5.00% 29.00% 38.00% 3.00% 10.00% 8.00% 8878492 54.90
en-nl_NL 46.00% 27.00% 19.00% 0.00% 49.00% 2.00% 3.00% 0.00% 36324231 85.95
en-da_DK 54.00% 31.00% 18.00% 5.00% 29.00% 5.00% 12.00% 7.00% 10738582 73.99
en-vi_VN 31.00% 18.00% 0.00% 13.00% 54.00% 1.00% 14.00% 6.00% 12394379 74.19
en-sv_SE 97.00% 91.00% 3.00% 3.00% 0.00% 3.00% 0.00% 0.00% 12544075 103.91
en-zh_CN 57.29% 22.92% 12.50% 21.88% 31.25% 1.04% 10.42% 1.04% 15181410 33.55
en-tr_TR 45.00% 14.50% 14.00% 16.50% 44.50% 5.00% 5.50% 4.00% 20282339 83.80
en-ja_XX 57.00% 35.00% 21.00% 1.00% 34.00% 6.00% 0.00% 0.00% 26201214 34.44
en-pt_XX 66.34% 36.63% 10.89% 18.81% 20.79% 3.96% 8.91% 0.00% 46525410 87.20
en-it_IT 36.00% 14.00% 18.00% 4.00% 60.00% 1.00% 3.00% 0.00% 58022366 97.44
en-de_DE 62.00% 29.00% 14.00% 19.00% 28.00% 2.00% 8.00% 2.00% 92597196 78.08
en-es_XX 58.42% 16.83% 25.74% 15.84% 22.77% 2.97% 15.84% 4.95% 98351611 72.18
CCCCSCBXWLNLporn#sentencesavg target length
en-sz_PL 0.00% 0.00% 0.00% 0.00% 0.00% 8.33% 91.67% 0.00% 12 71.42
en-mt_MT 3.85% 0.00% 3.85% 0.00% 50.00% 26.92% 19.23% 0.00% 26 12.58
en-tz_MA 12.12% 6.06% 6.06% 0.00% 45.45% 36.36% 6.06% 0.00% 33 57.33
en-zz_TR 0.00% 0.00% 0.00% 0.00% 8.82% 61.76% 29.41% 0.00% 34 46.53
en-kg_AO 1.35% 0.00% 1.35% 0.00% 14.86% 2.70% 81.08% 0.00% 74 29.20
en-qa_MM 11.03% 5.88% 3.68% 1.47% 72.06% 3.68% 13.24% 0.00% 136 55.28
en-bm_ML 6.04% 4.03% 2.01% 0.00% 26.85% 6.71% 60.40% 0.00% 149 32.19
en-az_IR 6.93% 6.93% 0.00% 0.00% 20.79% 13.86% 58.42% 0.00% 158 115.85
en-qd_MM 7.92% 4.95% 1.98% 0.99% 81.19% 3.96% 6.93% 0.00% 179 60.34
en-ay_BO 51.00% 33.00% 18.00% 0.00% 29.00% 3.00% 17.00% 0.00% 475 92.19
en-ak_GH 14.23% 13.60% 0.63% 0.00% 46.86% 19.25% 19.67% 0.00% 478 45.85
en-st_ZA 48.57% 42.14% 0.00% 6.43% 40.71% 1.43% 9.29% 0.00% 904 111.83
en-ve_ZA 60.40% 29.70% 21.78% 8.91% 28.71% 3.96% 6.93% 0.00% 1555 82.99
en-ts_ZA 51.49% 34.65% 11.88% 4.95% 40.59% 2.97% 4.95% 0.00% 1967 73.93
en-or_IN 42.61% 6.09% 24.35% 12.17% 38.26% 9.57% 9.57% 0.00% 5526 71.39
en-ns_ZA 4.00% 2.00% 0.00% 2.00% 23.00% 15.00% 58.00% 4.00% 14138 33.52
en-lg_UG 6.00% 0.00% 6.00% 0.00% 68.00% 17.00% 9.00% 2.00% 14701 15.83
en-ln_CD 8.00% 4.00% 3.00% 1.00% 14.00% 4.00% 74.00% 4.00% 21562 28.80
en-om_KE 2.00% 2.00% 0.00% 0.00% 31.00% 38.00% 29.00% 24.00% 22206 23.83
en-ss_SZ 12.65% 9.04% 3.61% 0.00% 13.25% 24.10% 50.00% 13.86% 22960 25.30
en-te_IN_rom 0.00% 0.00% 0.00% 0.00% 25.00% 8.00% 67.00% 5.00% 25272 24.21
en-cb_IQ 4.00% 1.00% 3.00% 0.00% 30.00% 18.00% 48.00% 11.00% 52297 30.04
en-tn_BW 0.00% 0.00% 0.00% 0.00% 6.90% 8.97% 63.45% 10.34% 71253 16.80
en-ff_NG 0.00% 0.00% 0.00% 0.00% 0.00% 8.00% 92.00% 2.00% 73022 33.59
en-sn_ZW 5.00% 1.00% 3.00% 1.00% 81.00% 14.00% 0.00% 0.00% 86868 102.59
en-wo_SN 0.00% 0.00% 0.00% 0.00% 1.71% 3.31% 94.98% 18.46% 88441 27.25
en-br_FR 17.00% 3.00% 1.00% 13.00% 37.00% 14.00% 32.00% 1.00% 115128 41.68
en-zu_ZA 55.00% 39.00% 3.00% 13.00% 30.00% 7.00% 8.00% 3.00% 126101 79.32
en-ku_TR 36.52% 12.17% 13.04% 11.30% 33.04% 28.70% 1.74% 1.74% 137874 90.51
en-ig_NG 58.00% 49.00% 3.00% 6.00% 29.00% 12.00% 1.00% 0.00% 148146 83.42
en-kn_IN 46.00% 9.00% 6.00% 31.00% 46.00% 2.00% 5.00% 4.00% 163921 70.20
en-yo_NG 34.93% 6.16% 10.96% 17.81% 34.93% 12.33% 17.81% 0.00% 175192 75.01
en-ky_KG 44.12% 24.51% 17.65% 1.96% 33.33% 22.55% 0.00% 0.98% 240657 69.56
en-tg_TJ 46.08% 18.63% 24.51% 2.94% 32.35% 20.59% 0.98% 4.90% 251865 75.31
en-ha_NG 30.00% 25.00% 3.00% 2.00% 49.00% 9.00% 12.00% 1.00% 339176 60.78
en-am_ET 59.11% 35.47% 2.46% 21.18% 37.44% 2.96% 0.49% 0.00% 346517 58.29
en-km_KH 56.12% 12.24% 33.67% 10.20% 42.86% 1.02% 0.00% 0.00% 412381 71.35
en-ne_NP 47.00% 10.00% 13.00% 24.00% 15.00% 8.00% 30.00% 14.00% 487155 79.14
en-su_ID 35.00% 15.00% 15.00% 5.00% 13.00% 13.00% 39.00% 0.00% 494142 57.08
en-ur_PK_rom 0.50% 0.00% 0.50% 0.00% 18.91% 27.36% 53.23% 5.47% 513123 18.41
en-ht_HT 55.67% 8.25% 10.31% 37.11% 35.05% 6.19% 3.09% 1.03% 558167 101.95
en-mn_MN 33.00% 8.00% 14.00% 11.00% 42.00% 7.00% 18.00% 12.00% 566885 44.43
en-te_IN 69.00% 42.00% 11.00% 16.00% 27.00% 1.00% 3.00% 1.00% 581651 97.95
en-kk_KZ 68.32% 40.59% 18.81% 8.91% 18.81% 8.91% 3.96% 1.98% 689651 72.36
en-be_BY 90.00% 57.00% 13.00% 20.00% 10.00% 0.00% 0.00% 2.00% 1125772 118.45
en-af_ZA 63.00% 40.00% 23.00% 0.00% 31.00% 2.00% 4.00% 12.00% 1504061 105.45
en-jv_ID 5.05% 1.01% 1.01% 3.03% 25.25% 10.10% 59.60% 8.08% 1513974 18.34
en-hi_IN_rom 1.00% 0.00% 0.00% 1.00% 39.00% 21.00% 39.00% 8.00% 3789571 18.13
en-lv_LV 59.00% 37.00% 9.00% 13.00% 31.00% 7.00% 3.00% 14.00% 4850957 83.67
en-ar_AR_rom 0.00% 0.00% 0.00% 0.00% 0.00% 4.00% 96.00% 4.00% 5584724 16.69
en-tl_XX 13.00% 6.00% 3.00% 4.00% 24.00% 26.00% 37.00% 5.00% 6593250 37.03
en-uk_UA 63.00% 42.00% 8.00% 13.00% 35.00% 1.00% 1.00% 5.00% 8547348 67.88
en-zh_TW 46.00% 11.00% 31.00% 4.00% 47.00% 6.00% 1.00% 1.00% 8778971 24.89
en-el_GR 49.00% 15.00% 5.00% 29.00% 38.00% 3.00% 10.00% 8.00% 8878492 54.90
en-nl_NL 46.00% 27.00% 19.00% 0.00% 49.00% 2.00% 3.00% 0.00% 36324231 85.95
en-da_DK 54.00% 31.00% 18.00% 5.00% 29.00% 5.00% 12.00% 7.00% 10738582 73.99
en-vi_VN 31.00% 18.00% 0.00% 13.00% 54.00% 1.00% 14.00% 6.00% 12394379 74.19
en-sv_SE 97.00% 91.00% 3.00% 3.00% 0.00% 3.00% 0.00% 0.00% 12544075 103.91
en-zh_CN 57.29% 22.92% 12.50% 21.88% 31.25% 1.04% 10.42% 1.04% 15181410 33.55
en-tr_TR 45.00% 14.50% 14.00% 16.50% 44.50% 5.00% 5.50% 4.00% 20282339 83.80
en-ja_XX 57.00% 35.00% 21.00% 1.00% 34.00% 6.00% 0.00% 0.00% 26201214 34.44
en-pt_XX 66.34% 36.63% 10.89% 18.81% 20.79% 3.96% 8.91% 0.00% 46525410 87.20
en-it_IT 36.00% 14.00% 18.00% 4.00% 60.00% 1.00% 3.00% 0.00% 58022366 97.44
en-de_DE 62.00% 29.00% 14.00% 19.00% 28.00% 2.00% 8.00% 2.00% 92597196 78.08
en-es_XX 58.42% 16.83% 25.74% 15.84% 22.77% 2.97% 15.84% 4.95% 98351611 72.18
Table 12:

Audit results for a sample of 100 sentences from WikiMatrix for each language pair, compared to the number of sentences available in the dataset. Language codes are as originally published. The length is measured in number of characters and averaged across the audited portion of each corpus. Languages with less than 20% correct sentences are boldfaced.

CCCCSCBXWLNLporn# sentencesavg target length
en-ug 12.87% 8.91% 1.98% 1.98% 72.28% 9.90% 1.98% 0.00% 22012 95.55
en-mwl 27.00% 26.00% 0.00% 1.00% 73.00% 0.00% 0.00% 0.00% 33899 135.26
en-tg 0.00% 0.00% 0.00% 0.00% 95.10% 3.92% 0.98% 0.00% 37975 88.87
en-ne 13.00% 7.00% 6.00% 0.00% 60.00% 23.00% 4.00% 0.00% 40549 69.26
en-ka 11.88% 2.97% 2.97% 5.94% 73.27% 10.89% 2.97% 0.00% 41638 144.74
en-lmo 12.75% 11.76% 0.00% 0.98% 81.37% 4.90% 0.98% 0.00% 43790 89.38
en-io 28.00% 27.00% 0.00% 1.00% 69.00% 2.00% 1.00% 0.00% 45999 83.26
en-jv 13.73% 9.80% 0.00% 3.92% 70.59% 12.75% 2.94% 0.00% 48301 91.87
en-wuu 23.23% 14.14% 7.07% 2.02% 65.66% 7.07% 4.04% 0.00% 51024 34.77
br-en 8.70% 7.61% 1.09% 0.00% 82.61% 4.35% 0.00% 0.00% 58400 90.68
bar-en 6.00% 6.00% 0.00% 0.00% 75.00% 16.00% 3.00% 0.00% 67394 103.51
en-kk 5.00% 2.00% 2.00% 1.00% 81.00% 14.00% 0.00% 0.00% 109074 56.03
en-sw 33.33% 27.27% 4.04% 2.02% 64.65% 2.02% 0.00% 0.00% 138590 111.61
en-nds 1.96% 1.96% 0.00% 0.00% 95.10% 1.96% 0.98% 0.00% 178533 91.95
be-en 26.00% 24.00% 2.00% 0.00% 73.00% 1.00% 0.00% 0.00% 257946 121.22
en-hi 36.27% 32.35% 0.98% 2.94% 59.80% 0.98% 2.94% 0.00% 696125 96.77
en-ko 48.04% 33.33% 2.94% 11.76% 48.04% 2.94% 0.98% 0.00% 1345630 55.18
en-uk 87.00% 84.00% 2.00% 1.00% 10.00% 1.00% 2.00% 0.00% 2576425 104.39
en-it 42.00% 42.00% 0.00% 0.00% 58.00% 0.00% 0.00% 0.00% 4626048 140.27
en-simple 37.62% 24.75% 0.00% 12.87% 56.44% 2.97% 2.97% 0.00% N/A 77.53
CCCCSCBXWLNLporn# sentencesavg target length
en-ug 12.87% 8.91% 1.98% 1.98% 72.28% 9.90% 1.98% 0.00% 22012 95.55
en-mwl 27.00% 26.00% 0.00% 1.00% 73.00% 0.00% 0.00% 0.00% 33899 135.26
en-tg 0.00% 0.00% 0.00% 0.00% 95.10% 3.92% 0.98% 0.00% 37975 88.87
en-ne 13.00% 7.00% 6.00% 0.00% 60.00% 23.00% 4.00% 0.00% 40549 69.26
en-ka 11.88% 2.97% 2.97% 5.94% 73.27% 10.89% 2.97% 0.00% 41638 144.74
en-lmo 12.75% 11.76% 0.00% 0.98% 81.37% 4.90% 0.98% 0.00% 43790 89.38
en-io 28.00% 27.00% 0.00% 1.00% 69.00% 2.00% 1.00% 0.00% 45999 83.26
en-jv 13.73% 9.80% 0.00% 3.92% 70.59% 12.75% 2.94% 0.00% 48301 91.87
en-wuu 23.23% 14.14% 7.07% 2.02% 65.66% 7.07% 4.04% 0.00% 51024 34.77
br-en 8.70% 7.61% 1.09% 0.00% 82.61% 4.35% 0.00% 0.00% 58400 90.68
bar-en 6.00% 6.00% 0.00% 0.00% 75.00% 16.00% 3.00% 0.00% 67394 103.51
en-kk 5.00% 2.00% 2.00% 1.00% 81.00% 14.00% 0.00% 0.00% 109074 56.03
en-sw 33.33% 27.27% 4.04% 2.02% 64.65% 2.02% 0.00% 0.00% 138590 111.61
en-nds 1.96% 1.96% 0.00% 0.00% 95.10% 1.96% 0.98% 0.00% 178533 91.95
be-en 26.00% 24.00% 2.00% 0.00% 73.00% 1.00% 0.00% 0.00% 257946 121.22
en-hi 36.27% 32.35% 0.98% 2.94% 59.80% 0.98% 2.94% 0.00% 696125 96.77
en-ko 48.04% 33.33% 2.94% 11.76% 48.04% 2.94% 0.98% 0.00% 1345630 55.18
en-uk 87.00% 84.00% 2.00% 1.00% 10.00% 1.00% 2.00% 0.00% 2576425 104.39
en-it 42.00% 42.00% 0.00% 0.00% 58.00% 0.00% 0.00% 0.00% 4626048 140.27
en-simple 37.62% 24.75% 0.00% 12.87% 56.44% 2.97% 2.97% 0.00% N/A 77.53
Table 13:

Audit results for a sample of 100 sentences from ParaCrawl for each language pair, compared to the number of sentences available in the dataset. Language codes are as originally published. The length is measured in number of characters and averaged across the audited portion of each corpus.

CCCCSCBXWLNLporn# sentencesavg target length
en-so 80.81% 61.62% 1.01% 18.18% 14.14% 5.05% 0.00% 0.00% 14879 189.83
en-ps 72.00% 53.00% 9.00% 10.00% 17.00% 10.00% 0.00% 0.00% 26321 141.01
en-my 45.00% 9.00% 16.00% 20.00% 32.00% 9.00% 14.00% 0.00% 31374 147.07
en-km 76.00% 51.00% 13.00% 12.00% 18.00% 6.00% 0.00% 0.00% 65113 121.20
en-ne 73.00% 48.00% 1.00% 24.00% 23.00% 2.00% 0.00% 0.00% 92084 153.42
en-sw 85.00% 60.00% 15.00% 10.00% 11.00% 2.00% 2.00% 0.00% 132517 167.34
en-si 37.00% 31.00% 6.00% 0.00% 62.00% 0.00% 1.00% 0.00% 217407 123.06
en-nn 35.92% 24.27% 8.74% 2.91% 49.51% 13.59% 0.97% 0.00% 323519 56.24
es-eu 88.00% 66.00% 15.00% 7.00% 10.00% 1.00% 1.00% 0.00% 514610 121.31
es-gl 89.00% 46.00% 6.00% 37.00% 4.00% 7.00% 0.00% 0.00% 1222837 107.88
en-ru 81.00% 73.00% 6.00% 2.00% 19.00% 0.00% 0.00% 6.00% 5377911 101.28
en-bg 95.15% 85.44% 0.97% 8.74% 4.85% 0.00% 0.00% 0.97% 6470710 112.29
es-ca 80.00% 54.00% 19.00% 7.00% 11.00% 9.00% 0.00% 5.00% 6870183 107.21
en-el 91.59% 68.22% 0.93% 22.43% 7.48% 0.93% 0.00% 0.00% 9402646 135.66
en-pl 94.12% 76.47% 0.98% 16.67% 3.92% 1.96% 0.00% 0.98% 13744860 95.95
en-nl 49.00% 32.00% 17.00% 0.00% 46.00% 3.00% 2.00% 0.00% 31295016 95.05
en-pt 93.07% 92.08% 0.00% 0.99% 4.95% 1.98% 0.00% 0.00% 31486963 108.68
en-it 60.82% 36.08% 16.49% 8.25% 38.14% 0.00% 1.03% 0.00% 40798278 127.55
en-es 87.00% 54.00% 20.00% 13.00% 12.00% 0.00% 1.00% 0.50% 78662122 119.72
en-de 82.83% 64.65% 13.13% 5.05% 13.13% 3.03% 1.01% 0.00% 82638202 111.43
en-fr 89.62% 82.08% 4.72% 2.83% 10.38% 0.00% 0.00% 0.00% 104351522 144.20
CCCCSCBXWLNLporn# sentencesavg target length
en-so 80.81% 61.62% 1.01% 18.18% 14.14% 5.05% 0.00% 0.00% 14879 189.83
en-ps 72.00% 53.00% 9.00% 10.00% 17.00% 10.00% 0.00% 0.00% 26321 141.01
en-my 45.00% 9.00% 16.00% 20.00% 32.00% 9.00% 14.00% 0.00% 31374 147.07
en-km 76.00% 51.00% 13.00% 12.00% 18.00% 6.00% 0.00% 0.00% 65113 121.20
en-ne 73.00% 48.00% 1.00% 24.00% 23.00% 2.00% 0.00% 0.00% 92084 153.42
en-sw 85.00% 60.00% 15.00% 10.00% 11.00% 2.00% 2.00% 0.00% 132517 167.34
en-si 37.00% 31.00% 6.00% 0.00% 62.00% 0.00% 1.00% 0.00% 217407 123.06
en-nn 35.92% 24.27% 8.74% 2.91% 49.51% 13.59% 0.97% 0.00% 323519 56.24
es-eu 88.00% 66.00% 15.00% 7.00% 10.00% 1.00% 1.00% 0.00% 514610 121.31
es-gl 89.00% 46.00% 6.00% 37.00% 4.00% 7.00% 0.00% 0.00% 1222837 107.88
en-ru 81.00% 73.00% 6.00% 2.00% 19.00% 0.00% 0.00% 6.00% 5377911 101.28
en-bg 95.15% 85.44% 0.97% 8.74% 4.85% 0.00% 0.00% 0.97% 6470710 112.29
es-ca 80.00% 54.00% 19.00% 7.00% 11.00% 9.00% 0.00% 5.00% 6870183 107.21
en-el 91.59% 68.22% 0.93% 22.43% 7.48% 0.93% 0.00% 0.00% 9402646 135.66
en-pl 94.12% 76.47% 0.98% 16.67% 3.92% 1.96% 0.00% 0.98% 13744860 95.95
en-nl 49.00% 32.00% 17.00% 0.00% 46.00% 3.00% 2.00% 0.00% 31295016 95.05
en-pt 93.07% 92.08% 0.00% 0.99% 4.95% 1.98% 0.00% 0.00% 31486963 108.68
en-it 60.82% 36.08% 16.49% 8.25% 38.14% 0.00% 1.03% 0.00% 40798278 127.55
en-es 87.00% 54.00% 20.00% 13.00% 12.00% 0.00% 1.00% 0.50% 78662122 119.72
en-de 82.83% 64.65% 13.13% 5.05% 13.13% 3.03% 1.01% 0.00% 82638202 111.43
en-fr 89.62% 82.08% 4.72% 2.83% 10.38% 0.00% 0.00% 0.00% 104351522 144.20
Table 14:

Audit results for a sample of 100 sentences from mC4 for each language, compared to the number of sentences available in the dataset. Language codes are as originally published. The length is measured in number of characters and averaged across the audited portion of each corpus. Languages with less than 20% correct sentences are boldfaced.

CCCCSCBWLNLporn# sentencesavg length
yo 84.69% 71.43% 2.04% 11.22% 14.29% 1.02% 0.00% 46214 117.71
st 56.70% 42.27% 14.43% 0.00% 35.05% 8.25% 0.00% 66837 132.13
haw 44.90% 34.69% 1.02% 9.18% 33.67% 21.43% 1.02% 84312 129.99
ig 55.91% 41.73% 10.24% 3.94% 0.00% 44.09% 0.79% 92909 98.03
sm 60.20% 58.16% 2.04% 0.00% 27.55% 12.24% 0.00% 98467 126.42
ha 80.81% 79.80% 1.01% 0.00% 14.14% 5.05% 2.02% 247479 155.76
su 59.60% 58.59% 1.01% 0.00% 25.25% 15.15% 2.02% 280719 107.10
sn 36.63% 32.67% 2.97% 0.99% 58.42% 4.95% 0.00% 326392 145.59
mg 57.00% 57.00% 0.00% 0.00% 18.00% 25.00% 0.00% 345040 116.23
pa 78.30% 68.87% 3.77% 5.66% 4.72% 10.38% 0.00% 363399 134.43
ga 76.77% 58.59% 6.06% 12.12% 10.10% 13.13% 0.00% 465670 147.35
co 33.00% 29.00% 2.00% 2.00% 48.00% 19.00% 0.00% 494913 195.30
zu 51.00% 48.00% 2.00% 1.00% 30.00% 19.00% 0.00% 555458 137.81
jv 52.73% 19.09% 19.09% 14.55% 40.00% 7.27% 1.82% 581528 97.96
km 92.86% 92.86% 0.00% 0.00% 7.14% 0.00% 0.00% 756612 162.57
kn 85.15% 73.27% 3.96% 7.92% 2.97% 9.90% 0.00% 1056849 105.39
fy 56.73% 50.00% 3.85% 2.88% 39.42% 3.85% 0.00% 1104359 234.25
te 89.00% 76.00% 9.00% 4.00% 3.00% 8.00% 0.00% 1188243 108.49
la 82.31% 65.38% 6.15% 10.77% 10.00% 7.69% 0.00% 674463 67.25
be 92.04% 86.73% 2.65% 2.65% 4.42% 3.54% 0.00% 1742030 110.86
af 76.00% 76.00% 0.00% 0.00% 15.00% 9.00% 0.00% 2152243 99.52
lb 17.48% 17.48% 0.00% 0.00% 7.77% 74.76% 0.00% 2740336 481.68
ne 78.35% 77.32% 1.03% 0.00% 21.65% 0.00% 0.00% 2942785 102.88
sr 93.69% 85.59% 7.21% 0.90% 5.41% 0.00% 0.00% 3398483 131.72
gl 67.62% 57.14% 10.48% 0.00% 13.33% 17.14% 0.00% 4549465 151.45
bn 93.00% 86.00% 1.00% 6.00% 3.00% 4.00% 0.00% 7444098 92.60
mr 40.00% 35.24% 2.86% 1.90% 49.52% 10.48% 0.00% 7774331 281.94
sl 92.08% 82.18% 4.95% 4.95% 2.97% 4.95% 0.00% 8499456 149.45
hi 80.30% 76.77% 1.01% 2.53% 19.70% 0.00% 2.53% 18507273 105.54
bg 80.90% 75.88% 2.51% 2.51% 2.01% 17.09% 0.00% 23409799 93.86
uk 95.48% 81.41% 7.54% 6.53% 2.01% 2.51% 0.00% 38556465 116.79
ro 94.95% 78.79% 12.12% 4.04% 3.03% 2.02% 0.00% 45738857 130.08
sv 91.18% 84.31% 2.94% 3.92% 4.90% 3.92% 1.96% 8570979 114.45
zh 92.00% 87.00% 1.00% 4.00% 1.00% 7.00% 0.00% 54542308 94.77
ja 99.00% 89.00% 6.00% 4.00% 0.00% 1.00% 1.00% 87337884 59.94
tr 95.96% 88.89% 0.00% 7.07% 3.54% 0.51% 0.00% 87595290 152.75
nl 92.08% 85.15% 6.93% 0.00% 1.98% 5.94% 0.00% 96210458 103.67
pl 96.00% 82.00% 7.00% 7.00% 2.00% 2.00% 0.00% 126164277 170.70
pt 86.00% 79.00% 4.00% 3.00% 2.00% 12.00% 1.00% 169239084 133.51
it 92.00% 79.00% 9.00% 4.00% 1.00% 7.00% 0.00% 186404508 180.26
fr 92.00% 82.00% 7.00% 3.00% 1.00% 7.00% 0.00% 332674575 143.69
de 91.18% 77.45% 7.84% 5.88% 6.86% 1.96% 0.00% 397006993 107.71
ru 91.06% 69.11% 11.38% 10.57% 4.07% 4.88% 0.00% 755585265 109.28
en 93.94% 83.84% 8.08% 2.02% 1.01% 5.05% 0.00% 3079081989 130.97
bg_latn 9.09% 9.09% 0.00% 0.00% 51.52% 39.39% 1.01% N/A 139.92
ja_latn 13.00% 7.00% 4.00% 2.00% 60.00% 27.00% 0.00% N/A 218.92
ru_latn 36.45% 25.23% 10.28% 0.93% 34.58% 28.97% 0.93% N/A 123.14
zh_latn 5.00% 4.00% 1.00% 0.00% 64.00% 31.00% 0.00% N/A 186.84
CCCCSCBWLNLporn# sentencesavg length
yo 84.69% 71.43% 2.04% 11.22% 14.29% 1.02% 0.00% 46214 117.71
st 56.70% 42.27% 14.43% 0.00% 35.05% 8.25% 0.00% 66837 132.13
haw 44.90% 34.69% 1.02% 9.18% 33.67% 21.43% 1.02% 84312 129.99
ig 55.91% 41.73% 10.24% 3.94% 0.00% 44.09% 0.79% 92909 98.03
sm 60.20% 58.16% 2.04% 0.00% 27.55% 12.24% 0.00% 98467 126.42
ha 80.81% 79.80% 1.01% 0.00% 14.14% 5.05% 2.02% 247479 155.76
su 59.60% 58.59% 1.01% 0.00% 25.25% 15.15% 2.02% 280719 107.10
sn 36.63% 32.67% 2.97% 0.99% 58.42% 4.95% 0.00% 326392 145.59
mg 57.00% 57.00% 0.00% 0.00% 18.00% 25.00% 0.00% 345040 116.23
pa 78.30% 68.87% 3.77% 5.66% 4.72% 10.38% 0.00% 363399 134.43
ga 76.77% 58.59% 6.06% 12.12% 10.10% 13.13% 0.00% 465670 147.35
co 33.00% 29.00% 2.00% 2.00% 48.00% 19.00% 0.00% 494913 195.30
zu 51.00% 48.00% 2.00% 1.00% 30.00% 19.00% 0.00% 555458 137.81
jv 52.73% 19.09% 19.09% 14.55% 40.00% 7.27% 1.82% 581528 97.96
km 92.86% 92.86% 0.00% 0.00% 7.14% 0.00% 0.00% 756612 162.57
kn 85.15% 73.27% 3.96% 7.92% 2.97% 9.90% 0.00% 1056849 105.39
fy 56.73% 50.00% 3.85% 2.88% 39.42% 3.85% 0.00% 1104359 234.25
te 89.00% 76.00% 9.00% 4.00% 3.00% 8.00% 0.00% 1188243 108.49
la 82.31% 65.38% 6.15% 10.77% 10.00% 7.69% 0.00% 674463 67.25
be 92.04% 86.73% 2.65% 2.65% 4.42% 3.54% 0.00% 1742030 110.86
af 76.00% 76.00% 0.00% 0.00% 15.00% 9.00% 0.00% 2152243 99.52
lb 17.48% 17.48% 0.00% 0.00% 7.77% 74.76% 0.00% 2740336 481.68
ne 78.35% 77.32% 1.03% 0.00% 21.65% 0.00% 0.00% 2942785 102.88
sr 93.69% 85.59% 7.21% 0.90% 5.41% 0.00% 0.00% 3398483 131.72
gl 67.62% 57.14% 10.48% 0.00% 13.33% 17.14% 0.00% 4549465 151.45
bn 93.00% 86.00% 1.00% 6.00% 3.00% 4.00% 0.00% 7444098 92.60
mr 40.00% 35.24% 2.86% 1.90% 49.52% 10.48% 0.00% 7774331 281.94
sl 92.08% 82.18% 4.95% 4.95% 2.97% 4.95% 0.00% 8499456 149.45
hi 80.30% 76.77% 1.01% 2.53% 19.70% 0.00% 2.53% 18507273 105.54
bg 80.90% 75.88% 2.51% 2.51% 2.01% 17.09% 0.00% 23409799 93.86
uk 95.48% 81.41% 7.54% 6.53% 2.01% 2.51% 0.00% 38556465 116.79
ro 94.95% 78.79% 12.12% 4.04% 3.03% 2.02% 0.00% 45738857 130.08
sv 91.18% 84.31% 2.94% 3.92% 4.90% 3.92% 1.96% 8570979 114.45
zh 92.00% 87.00% 1.00% 4.00% 1.00% 7.00% 0.00% 54542308 94.77
ja 99.00% 89.00% 6.00% 4.00% 0.00% 1.00% 1.00% 87337884 59.94
tr 95.96% 88.89% 0.00% 7.07% 3.54% 0.51% 0.00% 87595290 152.75
nl 92.08% 85.15% 6.93% 0.00% 1.98% 5.94% 0.00% 96210458 103.67
pl 96.00% 82.00% 7.00% 7.00% 2.00% 2.00% 0.00% 126164277 170.70
pt 86.00% 79.00% 4.00% 3.00% 2.00% 12.00% 1.00% 169239084 133.51
it 92.00% 79.00% 9.00% 4.00% 1.00% 7.00% 0.00% 186404508 180.26
fr 92.00% 82.00% 7.00% 3.00% 1.00% 7.00% 0.00% 332674575 143.69
de 91.18% 77.45% 7.84% 5.88% 6.86% 1.96% 0.00% 397006993 107.71
ru 91.06% 69.11% 11.38% 10.57% 4.07% 4.88% 0.00% 755585265 109.28
en 93.94% 83.84% 8.08% 2.02% 1.01% 5.05% 0.00% 3079081989 130.97
bg_latn 9.09% 9.09% 0.00% 0.00% 51.52% 39.39% 1.01% N/A 139.92
ja_latn 13.00% 7.00% 4.00% 2.00% 60.00% 27.00% 0.00% N/A 218.92
ru_latn 36.45% 25.23% 10.28% 0.93% 34.58% 28.97% 0.93% N/A 123.14
zh_latn 5.00% 4.00% 1.00% 0.00% 64.00% 31.00% 0.00% N/A 186.84
Table 15:

Audit results for a sample of 100 sentences from OSCAR for each language, compared to the number of sentences available in the dataset. If fewer than 100 sentences were available, all sentences were audited language codes are as originally published. Length is measured in number of characters. Languages with less than 20% correct sentences are boldfaced.

CCCCSCBWLNLporn# sentencesavg length
diq 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 131.00
bcl 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 623.00
cbk 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 519.00
pam 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 139.00
bar 25.00% 25.00% 0.00% 0.00% 0.00% 75.00% 0.00% 53.50
myv 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 127.00
yue 0.00% 0.00% 0.00% 0.00% 57.14% 42.86% 0.00% 177.00
mwl 57.14% 57.14% 0.00% 0.00% 42.86% 0.00% 0.00% 141.00
frr 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 231.56
ht 30.00% 30.00% 0.00% 0.00% 0.00% 70.00% 0.00% 10 329.10
ie 30.00% 30.00% 0.00% 0.00% 30.00% 40.00% 0.00% 11 121.70
scn 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 17 155.59
tyv 96.15% 96.15% 0.00% 0.00% 0.00% 3.85% 0.00% 26 167.96
mai 79.31% 75.86% 0.00% 3.45% 20.69% 0.00% 0.00% 29 141.17
bxr 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 37 160.76
dsb 100.00% 97.56% 0.00% 2.44% 0.00% 0.00% 0.00% 41 155.15
so 0.00% 0.00% 0.00% 0.00% 28.57% 71.43% 0.00% 42 208.24
rm 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 47 137.66
nah 100.00% 96.67% 0.00% 3.33% 0.00% 0.00% 0.00% 60 164.53
nap 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 61 152.11
yo 98.46% 96.92% 0.00% 1.54% 1.54% 0.00% 0.00% 64 281.57
gn 81.48% 81.48% 0.00% 0.00% 2.47% 16.05% 0.00% 81 234.95
vec 91.36% 91.36% 0.00% 0.00% 0.00% 8.64% 0.00% 81 184.90
kw 91.57% 90.36% 0.00% 1.20% 3.61% 4.82% 0.00% 83 162.75
wuu 0.00% 0.00% 0.00% 0.00% 98.84% 1.16% 0.00% 86 157.15
eml 42.57% 42.57% 0.00% 0.00% 0.00% 57.43% 0.00% 104 177.88
bh 89.42% 21.15% 0.00% 68.27% 1.92% 8.65% 0.00% 104 137.17
min 64.00% 6.00% 0.00% 58.00% 27.00% 9.00% 0.00% 180 649.85
qu 100.00% 98.97% 0.00% 1.03% 0.00% 0.00% 0.00% 425 167.27
su 99.00% 99.00% 0.00% 0.00% 0.00% 1.00% 0.00% 676 221.00
jv 97.00% 86.00% 0.00% 11.00% 1.00% 2.00% 0.00% 2350 203.08
als 93.00% 93.00% 0.00% 0.00% 6.00% 1.00% 0.00% 7997 375.44
la 98.00% 98.00% 0.00% 0.00% 2.00% 0.00% 0.00% 33838 224.11
uz 98.00% 98.00% 0.00% 0.00% 2.00% 0.00% 0.00% 34244 369.99
nds 97.03% 95.05% 0.00% 1.98% 2.97% 0.00% 0.00% 35032 344.74
sw 98.00% 98.00% 0.00% 0.00% 0.00% 2.00% 0.00% 40066 196.70
br 100.00% 96.00% 0.00% 4.00% 0.00% 0.00% 0.00% 61941 239.56
fy 97.00% 97.00% 0.00% 0.00% 2.00% 1.00% 0.00% 67762 340.23
am 81.09% 79.10% 0.00% 1.99% 18.91% 0.00% 0.00% 287142 267.43
af 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 517353 339.18
eu 100.00% 98.00% 0.00% 2.00% 0.00% 0.00% 0.00% 1099498 330.93
mn 98.00% 94.00% 0.00% 4.00% 2.00% 0.00% 0.00% 1430527 309.94
te 98.99% 93.94% 1.01% 4.04% 0.00% 1.01% 1.01% 1685185 412.31
kk 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 2719851 318.93
ca 99.00% 91.00% 0.00% 8.00% 1.00% 0.00% 0.00% 13292843 333.38
nl 98.00% 94.00% 2.00% 2.00% 2.00% 0.00% 4.00% 126067610 305.01
it 87.13% 71.29% 1.98% 13.86% 11.88% 0.99% 1.98% 210348435 393.66
zh 100.00% 97.00% 0.00% 3.00% 0.00% 0.00% 1.00% 232673578 195.60
fr 100.00% 93.00% 0.00% 7.00% 0.00% 0.00% 5.00% 461349575 306.62
es 100.00% 94.00% 0.00% 6.00% 0.00% 0.00% 3.00% 488616724 268.07
en 99.00% 96.00% 0.00% 3.00% 0.00% 1.00% 1.00% 3809525119 364.65
CCCCSCBWLNLporn# sentencesavg length
diq 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 131.00
bcl 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 623.00
cbk 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 519.00
pam 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 139.00
bar 25.00% 25.00% 0.00% 0.00% 0.00% 75.00% 0.00% 53.50
myv 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 127.00
yue 0.00% 0.00% 0.00% 0.00% 57.14% 42.86% 0.00% 177.00
mwl 57.14% 57.14% 0.00% 0.00% 42.86% 0.00% 0.00% 141.00
frr 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 231.56
ht 30.00% 30.00% 0.00% 0.00% 0.00% 70.00% 0.00% 10 329.10
ie 30.00% 30.00% 0.00% 0.00% 30.00% 40.00% 0.00% 11 121.70
scn 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 17 155.59
tyv 96.15% 96.15% 0.00% 0.00% 0.00% 3.85% 0.00% 26 167.96
mai 79.31% 75.86% 0.00% 3.45% 20.69% 0.00% 0.00% 29 141.17
bxr 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 37 160.76
dsb 100.00% 97.56% 0.00% 2.44% 0.00% 0.00% 0.00% 41 155.15
so 0.00% 0.00% 0.00% 0.00% 28.57% 71.43% 0.00% 42 208.24
rm 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 47 137.66
nah 100.00% 96.67% 0.00% 3.33% 0.00% 0.00% 0.00% 60 164.53
nap 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 61 152.11
yo 98.46% 96.92% 0.00% 1.54% 1.54% 0.00% 0.00% 64 281.57
gn 81.48% 81.48% 0.00% 0.00% 2.47% 16.05% 0.00% 81 234.95
vec 91.36% 91.36% 0.00% 0.00% 0.00% 8.64% 0.00% 81 184.90
kw 91.57% 90.36% 0.00% 1.20% 3.61% 4.82% 0.00% 83 162.75
wuu 0.00% 0.00% 0.00% 0.00% 98.84% 1.16% 0.00% 86 157.15
eml 42.57% 42.57% 0.00% 0.00% 0.00% 57.43% 0.00% 104 177.88
bh 89.42% 21.15% 0.00% 68.27% 1.92% 8.65% 0.00% 104 137.17
min 64.00% 6.00% 0.00% 58.00% 27.00% 9.00% 0.00% 180 649.85
qu 100.00% 98.97% 0.00% 1.03% 0.00% 0.00% 0.00% 425 167.27
su 99.00% 99.00% 0.00% 0.00% 0.00% 1.00% 0.00% 676 221.00
jv 97.00% 86.00% 0.00% 11.00% 1.00% 2.00% 0.00% 2350 203.08
als 93.00% 93.00% 0.00% 0.00% 6.00% 1.00% 0.00% 7997 375.44
la 98.00% 98.00% 0.00% 0.00% 2.00% 0.00% 0.00% 33838 224.11
uz 98.00% 98.00% 0.00% 0.00% 2.00% 0.00% 0.00% 34244 369.99
nds 97.03% 95.05% 0.00% 1.98% 2.97% 0.00% 0.00% 35032 344.74
sw 98.00% 98.00% 0.00% 0.00% 0.00% 2.00% 0.00% 40066 196.70
br 100.00% 96.00% 0.00% 4.00% 0.00% 0.00% 0.00% 61941 239.56
fy 97.00% 97.00% 0.00% 0.00% 2.00% 1.00% 0.00% 67762 340.23
am 81.09% 79.10% 0.00% 1.99% 18.91% 0.00% 0.00% 287142 267.43
af 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 517353 339.18
eu 100.00% 98.00% 0.00% 2.00% 0.00% 0.00% 0.00% 1099498 330.93
mn 98.00% 94.00% 0.00% 4.00% 2.00% 0.00% 0.00% 1430527 309.94
te 98.99% 93.94% 1.01% 4.04% 0.00% 1.01% 1.01% 1685185 412.31
kk 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 2719851 318.93
ca 99.00% 91.00% 0.00% 8.00% 1.00% 0.00% 0.00% 13292843 333.38
nl 98.00% 94.00% 2.00% 2.00% 2.00% 0.00% 4.00% 126067610 305.01
it 87.13% 71.29% 1.98% 13.86% 11.88% 0.99% 1.98% 210348435 393.66
zh 100.00% 97.00% 0.00% 3.00% 0.00% 0.00% 1.00% 232673578 195.60
fr 100.00% 93.00% 0.00% 7.00% 0.00% 0.00% 5.00% 461349575 306.62
es 100.00% 94.00% 0.00% 6.00% 0.00% 0.00% 3.00% 488616724 268.07
en 99.00% 96.00% 0.00% 3.00% 0.00% 1.00% 1.00% 3809525119 364.65
1

7

This surprisingly high number comes in part because there are many closely related languages, e.g., one person may be proficient enough to rate many different Slavic or Turkic languages even if only one is their native language.

8

Some languages had fewer than 100 sentences.

9

This is a result of the language code used by the Alemannic Wikipedia and affects any corpus or tool that uses Wikipedia data without correcting for this, like FastText.

10

Kudos to Rebecca Knowles for this explanation.

12

For the translation from English, BLEU scores are less comparable but the trend holds nonetheless, with values of (ρ = 0.32, p = 0.14), (ρ = 0.74, p = 0.000078), and (ρ = 0.80, p = 0.0000087), respectively.

13

The jw.org Web site seems to use correct BCP-47 extensions now, however, and entering a code such as “jw_dmr” redirects to “naq_x_dmr”.

željko
Agić
and
Ivan
Vulić
.
2019
.
JW300: A wide-coverage parallel corpus for low-resource languages
. In
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
, pages
3204
3210
,
Florence, Italy
.
Association for Computational Linguistics
.
Wissam
Antoun
,
Baly
, and
Hazem
Hajj
.
2021
.
AraELECTRA: Pre-training text discriminators for Arabic language understanding
. In
Proceedings of the Sixth Arabic Natural Language Processing Workshop
, pages
191
195
,
Kyiv, Ukraine (Virtual)
.
Association for Computational Linguistics
.
Naveen
Arivazhagan
,
Ankur
Bapna
,
Orhan
Firat
,
Dmitry
Lepikhin
,
Melvin
Johnson
,
Maxim
Krikun
,
Mia Xu
Chen
,
Yuan
Cao
,
George F.
Foster
,
Colin
Cherry
,
Wolfgang
Macherey
,
Zhifeng
Chen
, and
Yonghui
Wu
.
2019
.
Massively multilingual neural machine translation in the wild: Findings and challenges
.
arXiv preprint arXiv:1907.05019
.
Mikel
Artetxe
and
Holger
Schwenk
.
2019
.
Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond
.
Transactions of the Association for Computational Linguistics
,
7
:
597
610
.
Amittai
Axelrod
,
Xiaodong
He
, and
Jianfeng
Gao
.
2011
.
Domain adaptation via pseudo in-domain data selection
. In
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing
, pages
355
362
,
Edinburgh, Scotland, UK.
Association for Computational Linguistics
.
Marta
Bañón
,
Pinzhen
Chen
,
Barry
,
Kenneth
Heafield
,
Hieu
Hoang
,
Miquel
Esplà-Gomis
,
Mikel L.
,
Amir
Kamran
,
Faheem
Kirefu
,
Philipp
Koehn
,
Sergio Ortiz
Rojas
,
Leopoldo Pla
Sempere
,
Gema
Ramírez-Sánchez
,
Elsa
Sarrías
,
Marek
Strelec
,
Brian
Thompson
,
William
Waites
,
Dion
Wiggins
, and
Jaume
Zaragoza
.
2020
.
ParaCrawl: Web-scale acquisition of parallel corpora
. In
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
, pages
4555
4567
,
Online
.
Association for Computational Linguistics
.
Emily M.
Bender
and
Batya
Friedman
.
2018
.
Data statements for natural language processing: Toward mitigating system bias and enabling better science
.
Transactions of the Association for Computational Linguistics
,
6
:
587
604
.
Emily M.
Bender
,
Timnit
Gebru
,
Angelina
McMillan-Major
, and
Shmargaret
Shmitchell
.
2021
.
On the dangers of stochastic parrots: Can language models be too big?
In
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
, pages
610
623
,
New York, NY, USA
.
Association for Computing Machinery
.
Stella
Biderman
and
Walter J.
Scheirer
.
2020
.
Pitfalls in machine learning research: Reexamining the development cycle
.
arXiv preprint arXiv:2011.02832
.
Abeba
Birhane
and
Vinay Uday
Prabhu
.
2021
.
Large image datasets: A pyrrhic win for computer vision?
In
2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
, pages
1536
1546
.
Jan A.
Botha
,
Emily
Pitler
,
Ji
Ma
,
Anton
Bakalov
,
Alex
Salcianu
,
David
Weiss
,
Ryan
McDonald
, and
Slav
Petrov
.
2017
.
Natural language processing with small feed-forward networks
. In
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
, pages
2879
2885
,
Copenhagen, Denmark
.
Association for Computational Linguistics
.
Tom
Brown
,
Benjamin
Mann
,
Nick
Ryder
,
Melanie
Subbiah
,
Jared D.
Kaplan
,
Prafulla
Dhariwal
,
Arvind
Neelakantan
,
Pranav
Shyam
,
Girish
Sastry
,
Amanda
,
Sandhini
Agarwal
,
Ariel
Herbert-Voss
,
Gretchen
Krueger
,
Tom
Henighan
,
Rewon
Child
,
Ramesh
,
Daniel
Ziegler
,
Jeffrey
Wu
,
Clemens
Winter
,
Chris
Hesse
,
Mark
Chen
,
Eric
Sigler
,
Mateusz
Litwin
,
Scott
Gray
,
Benjamin
Chess
,
Jack
Clark
,
Christopher
Berner
,
Sam
McCandlish
,
Alec
,
Ilya
Sutskever
, and
Dario
Amodei
.
2020
.
Language models are few-shot learners
. In
Advances in Neural Information Processing Systems
, volume
33
, pages
1877
1901
.
Curran Associates, Inc.
Isaac
Caswell
,
Theresa
Breiner
,
Daan van
Esch
, and
Ankur
Bapna
.
2020
.
Language ID in the wild: Unexpected challenges on the path to a thousand-language web text corpus
. In
Proceedings of the 28th International Conference on Computational Linguistics
, pages
6588
6608
,
Barcelona, Spain (Online)
.
International Committee on Computational Linguistics
.
Branden
Chan
,
Stefan
Schweter
, and
Timo
Möller
.
2020
.
German’s next language model
. In
Proceedings of the 28th International Conference on Computational Linguistics
, pages
6788
6796
,
Barcelona, Spain (Online)
.
International Committee on Computational Linguistics
.
Avihay
Chriqui
and
Inbal
Yahav
.
2021
.
HeBERT & HebEMO: A Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition
.
arXiv preprint arXiv:2102.01909
.
Alexis
Conneau
,
Kartikay
Khandelwal
,
Naman
Goyal
,
Vishrav
Chaudhary
,
Guillaume
Wenzek
,
Francisco
Guzmán
,
Edouard
Grave
,
Myle
Ott
,
Luke
Zettlemoyer
, and
Veselin
Stoyanov
.
2020
.
Unsupervised cross-lingual representation learning at scale
. In
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
, pages
8440
8451
,
Online
.
Association for Computational Linguistics
.
Alexis
Conneau
,
Ruty
Rinott
,
Guillaume
Lample
,
Williams
,
Samuel
Bowman
,
Holger
Schwenk
, and
Veselin
Stoyanov
.
2018
.
XNLI: Evaluating cross-lingual sentence representations
. In
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
, pages
2475
2485
,
Brussels, Belgium
.
Association for Computational Linguistics
.
Pieter
Delobelle
,
Thomas
Winters
, and
Bettina
Berendt
.
2020
.
RobBERT: a Dutch RoBERTa-based Language Model
. In
Findings of the Association for Computational Linguistics: EMNLP 2020
, pages
3255
3265
,
Online
.
Association for Computational Linguistics
.
Jacob
Devlin
,
Ming-Wei
Chang
,
Kenton
Lee
, and
Kristina
Toutanova
.
2019
.
BERT: Pre-training of deep bidirectional transformers for language understanding
. In
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
, pages
4171
4186
,
Minneapolis, Minnesota
.
Association for Computational Linguistics
.
Jesse
Dodge
,
Maarten
Sap
,
Ana
Marasovic
,
William
Agnew
,
Gabriel
Ilharco
,
Dirk
Groeneveld
, and
Matt
Gardner
.
2021
.
Documenting the english colossal clean crawled corpus
.
arXiv preprint arXiv:2104.08758
.
Stefan
Dumitrescu
,
Andrei-Marius
Avram
, and
Sampo
Pyysalo
.
2020
.
The birth of Romanian BERT
. In
Findings of the Association for Computational Linguistics: EMNLP 2020
, pages
4324
4328
,
Online
.
Association for Computational Linguistics
.
Ahmed
El-Kishky
,
Vishrav
Chaudhary
,
Francisco
Guzmán
, and
Philipp
Koehn
.
2020
.
CCAligned: A massive collection of cross-lingual web-document pairs
. In
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
, pages
5960
5969
,
Online
.
Association for Computational Linguistics
.
Miquel
Esplà
,
Mikel
,
Gema
Ramírez-Sánchez
, and
Hieu
Hoang
.
2019
.
ParaCrawl: Web-scale parallel corpora for the languages of the EU
. In
Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks
, pages
118
119
,
Dublin, Ireland
.
European Association for Machine Translation
.
Angela
Fan
,
Shruti
Bhosale
,
Holger
Schwenk
,
Zhiyi
Ma
,
Ahmed
El-Kishky
,
Siddharth
Goyal
,
Mandeep
Baines
,
Onur
Celebi
,
Guillaume
Wenzek
,
Vishrav
Chaudhary
,
Naman
Goyal
,
Tom
Birch
,
Vitaliy
Liptchinsky
,
Sergey
Edunov
,
Edouard
Grave
,
Michael
Auli
, and
Armand
Joulin
.
2020
.
Beyond English-centric multilingual machine translation
.
arXiv preprint arXiv:2010.11125
.
Wilhelmina
Nekoto
,
Vukosi
Marivate
,
Tshinondiwa
Matsila
,
Timi
Fasubaa
,
Taiwo
Fagbohungbe
,
Solomon Oluwole
Akinola
,
Shamsuddeen
,
Salomon Kabongo
Kabenamualu
,
Salomey
Osei
,
Freshia
Sackey
,
Rubungo Andre
Niyongabo
,
Ricky
Macharm
,
Perez
Ogayo
,
Orevaoghene
Ahia
,
MusieMeressa
Berhe
,
Mofetoluwa
,
Masabata
Mokgesi-Selinga
,
Lawrence
Okegbemi
,
Laura
Martinus
,
Kolawole
Tajudeen
,
Kevin
Degila
,
Kelechi
Ogueji
,
Kathleen
Siminyu
,
Julia
Kreutzer
,
Jason
Webster
,
Jamiil Toure
Ali
,
Abbott
,
Iroro
Orife
,
Ignatius
Ezeani
,
Dangana
,
Herman
Kamper
,
Elsahar
,
Goodness
Duru
,
Ghollah
Kioko
,
Murhabazi
Espoir
,
Elan van
Biljon
,
Daniel
Whitenack
,
Christopher
Onyefuluchi
,
Chris Chinenye
Emezue
,
Bonaventure F. P.
Dossou
,
Blessing
Sibanda
,
Blessing
Bassey
,
Ayodele
Olabiyi
,
Arshath
Ramkilowan
,
Alp
Öktem
,
, and
Abdallah
Bashir
.
2020
.
Participatory research for low-resourced machine translation: A case study in African languages
. In
Findings of the Association for Computational Linguistics: EMNLP 2020
.
Online
.
Leo
Gao
,
Stella
Biderman
,
Sid
Black
,
Laurence
Golding
,
Travis
Hoppe
,
Charles
Foster
,
Jason
Phang
,
Horace
He
,
Anish
Thite
,
Noa
Nabeshima
,
Shawn
Presser
and
Connor
Leahy
.
2020
.
The pile: An 800gb dataset of diverse text for language modeling
.
arXiv preprint arXiv:2101.00027
.
Timnit
Gebru
,
Jamie
Morgenstern
,
Briana
Vecchione
,
Jennifer Wortman
Vaughan
,
Hanna
Wallach
,
Hal Daumé
III
, and
Kate
Crawford
.
2018
.
Datasheets for datasets
.
arXiv preprint arXiv:1803.09010
.
Naman
Goyal
,
Cynthia
Gao
,
Vishrav
Chaudhary
,
Peng-Jen
Chen
,
Guillaume
Wenzek
,
Da
Ju
,
Sanjana
Krishnan
,
Marc’Aurelio
Ranzato
,
Francisco
Guzmán
, and
Angela
Fan
.
2021
.
The FLORES-101 evaluation benchmark for low-resource and multilingual machine translation
.
arXiv preprint arXiv:2106.03193
.
Edouard
Grave
,
Piotr
Bojanowski
,
Prakhar
Gupta
,
Armand
Joulin
, and
Tomas
Mikolov
.
2018
.
Learning word vectors for 157 languages
. In
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
,
Miyazaki, Japan
.
European Language Resources Association (ELRA)
.
Sarah
Holland
,
Ahmed
Hosny
,
Sarah
Newman
,
Joshua
Joseph
, and
Kasia
Chmielinski
.
2018
.
The dataset nutrition label: A framework to drive higher data quality standards
.
arXiv preprint arXiv:1805.03677
.
Junjie
Hu
,
Sebastian
Ruder
,
Siddhant
,
Graham
Neubig
,
Orhan
Firat
, and
Melvin
Johnson
.
2020
.
XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation
. In
Proceedings of the 37th International Conference on Machine Learning
,
volume 119 of Proceedings of Machine Learning Research
, pages
4411
4421
.
PMLR
.
Armand
Joulin
,
Edouard
Grave
,
Piotr
Bojanowski
,
Matthijs
Douze
,
Hervé
Jégou
, and
Tomás
Mikolov
.
2016
.
Fasttext.zip: Compressing text classification models
.
arXiv preprint arXiv:1612.03651
.
Armand
Joulin
,
Edouard
Grave
,
Piotr
Bojanowski
, and
Tomas
Mikolov
.
2017
.
Bag of tricks for efficient text classification
. In
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
, pages
427
431
,
Valencia, Spain
.
Association for Computational Linguistics
.
Marcin
Junczys-Dowmunt
.
2018
.
Dual conditional cross-entropy filtering of noisy parallel corpora
. In
Proceedings of the Third Conference on Machine Translation: Shared Task Papers
, pages
888
895
,
Belgium, Brussels
.
Association for Computational Linguistics
.
Marcin
Junczys-Dowmunt
.
2019
.
Microsoft translator at WMT 2019: Towards large-scale document-level neural machine translation
. In
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
, pages
225
233
,
Florence, Italy
.
Association for Computational Linguistics
.
Divyanshu
Kakwani
,
Anoop
Kunchukuttan
,
Satish
Golla
,
Gokul
N. C.
,
Avik
Bhattacharyya
,
Mitesh M.
Khapra
, and
Pratyush
Kumar
.
2020
.
IndicNLPSuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for Indian languages
. In
Findings of the Association for Computational Linguistics: EMNLP 2020
, pages
4948
4961
,
Association for Computational Linguistics
,
Online
.
David
Kamholz
,
Jonathan
Pool
, and
Susan
Colowick
.
2014
.
PanLex: Building a resource for panlingual lexical translation
. In
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
, pages
3145
3150
,
Reykjavik, Iceland
.
European Language Resources Association (ELRA)
.
Vincentius
Kevin
,
Birte
Högden
,
Claudia
Schwenger
,
Ali
Şahan
,
Neelu
,
Piush
Aggarwal
,
Anusha
Bangaru
,
Farid
, and
Ahmet
Aker
.
2018
.
Information nutrition labels: A plugin for online news evaluation
. In
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
, pages
28
33
,
Brussels, Belgium
.
Association for Computational Linguistics
.
Huda
Khayrallah
and
Philipp
Koehn
.
2018
.
On the impact of various types of noise on neural machine translation
. In
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation
, pages
74
83
,
Melbourne, Australia
.
Association for Computational Linguistics
.
Philipp
Koehn
,
Vishrav
Chaudhary
,
Ahmed
El-Kishky
,
Naman
Goyal
,
Peng-Jen
Chen
, and
Francisco
Guzmán
.
2020
.
Findings of the WMT 2020 shared task on parallel corpus filtering and alignment
. In
Proceedings of the Fifth Conference on Machine Translation
, pages
726
742
,
Online
.
Association for Computational Linguistics
.
John
Koutsikakis
,
Ilias
Chalkidis
,
Prodromos
Malakasiotis
, and
Ion
Androutsopoulos
.
2020
.
Greek-bert: The greeks visiting sesame street
. In
11th Hellenic Conference on Artificial Intelligence
,
SETN 2020
, pages
110
117
,
New York, NY, USA
.
Association for Computing Machinery
.
Alexandra Sasha
Luccioni
and
Joseph D.
Viviano
.
2021
.
What’s in the box? an analysis of undesirable content in the common crawl corpus
.
arXiv preprint arXiv:2105.02732
.
Louis
Martin
,
Benjamin
Muller
,
Pedro Javier Ortiz
Suárez
,
Yoann
Dupont
,
Laurent
Romary
,
Éric
de la Clergerie
,
Djamé
Seddah
, and
Benoît
Sagot
.
2020
.
CamemBERT: A tasty French language model
. In
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
, pages
7203
7219
,
Online
.
Association for Computational Linguistics
.
Mihai
Masala
,
Stefan
Ruseti
, and
Mihai
Dascalu
.
2020
.
RoBERT—a Romanian BERT model
. In
Proceedings of the 28th International Conference on Computational Linguistics
, pages
6626
6637
,
Barcelona, Spain (Online)
.
International Committee on Computational Linguistics
.
Robert C.
Moore
and
William
Lewis
.
2010
.
Intelligent selection of language model training data
. In
Proceedings of the ACL 2010 Conference Short Papers
, pages
220
224
,
Uppsala, Sweden
.
Association for Computational Linguistics
.
Pedro Javier Ortiz
Suárez
,
Laurent
Romary
, and
Benoît
Sagot
.
2020
.
A monolingual approach to contextualized word embeddings for mid-resource languages
. In
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
, pages
1703
1714
,
Online
.
Association for Computational Linguistics
.
Pedro Javier Ortiz
Suárez
,
Benoît
Sagot
, and
Laurent
Romary
.
2019
.
Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures
. In
Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019
, pages
9
16
,
Mannheim
.
Leibniz-Institut für Deutsche Sprache
.
Phillips
and
Mark
Davis
.
2005
.
Tags for Identifying Languages
.
Internet Engineering Task Force. Work in Progress
.
Ye
Qi
,
Devendra
Sachan
,
Matthieu
Felix
,
Sarguna
, and
Graham
Neubig
.
2018
.
When and why are pre-trained word embeddings useful for neural machine translation?
In
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
, pages
529
535
,
New Orleans, Louisiana
.
Association for Computational Linguistics
.
Colin
Raffel
,
Noam
Shazeer
,
Roberts
,
Katherine
Lee
,
Sharan
Narang
,
Michael
Matena
,
Yanqi
Zhou
,
Wei
Li
, and
Peter J.
Liu
.
2020
.
Exploring the limits of transfer learning with a unified text-to-text transformer
.
Journal of Machine Learning Research
,
21
:
1
67
.
Spencer
Rarrick
,
Chris
Quirk
, and
Will
Lewis
.
2011
.
MT detection in Web-scraped parallel corpora
. In
Proceedings of MT Summit XIII
.
Asia-Pacific Association for Machine Translation
.
Holger
Schwenk
,
Vishrav
Chaudhary
,
Shuo
Sun
,
Hongyu
Gong
, and
Francisco
Guzmán
.
2021
.
WikiMatrix: Mining 135M parallel sentences in 1620 language pairs from Wikipedia
. In
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
, pages
1351
1361
,
Online
.
Association for Computational Linguistics
.
Amit
Seker
,
Elron
Bandel
,
Dan
Bareket
,
Idan
Brusilovsky
,
Refael Shaked
Greenfeld
, and
Reut
Tsarfaty
.
2021
.
AlephBERT:A Hebrew large pre-trained language model to start-off your Hebrew NLP application with
.
arXiv preprint arXiv:2104.04052
.
Linda J.
Skitka
,
Kathleen L.
Mosier
, and
Mark
Burdick
.
1999
.
Does automation bias decision-making?
International Journal of Human-Computer Studies
,
51
(
5
):
991
1006
.
Chenkai
Sun
,
Abolfazl
Asudeh
,
H. V.
,
Bill
Howe
, and
Julia
Stoyanovich
.
2019
.
Mithralabel: Flexible dataset nutritional labels for responsible data science
. In
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
, pages
2893
2896
,
New York, NY, USA
.
Association for Computing Machinery
.
Wei
Wang
,
Taro
Watanabe
,
Macduff
Hughes
,
Tetsuji
Nakagawa
, and
Ciprian
Chelba
.
2018
.
Denoising neural machine translation training with trusted data and online data selection
. In
Proceedings of the Third Conference on Machine Translation: Research Papers
, pages
133
143
,
Brussels, Belgium
.
Association for Computational Linguistics
.
Bryan
Wilie
,
Karissa
Vincentio
,
Genta Indra
Winata
,
Samuel
Cahyawijaya
,
Xiaohong
Li
,
Zhi Yuan
Lim
,
Sidik
Soleman
,
Mahendra
,
Pascale
Fung
,
Syafri
Bahar
, and
Ayu
Purwarianti
.
2020
.
IndoNLU: Benchmark and resources for evaluating Indonesian natural language understanding
. In
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
, pages
843
857
,
Suzhou, China
.
Association for Computational Linguistics
.
Hainan
Xu
and
Philipp
Koehn
.
2017
.
Zipporah: A fast and scalable data cleaning system for noisy Web-crawled parallel corpora
. In
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
, pages
2945
2950
,
Copenhagen, Denmark
.
Association for Computational Linguistics
.
Linting
Xue
,
Noah
Constant
,
Roberts
,
Mihir
Kale
,
Rami
Al-Rfou
,
Siddhant
,
Barua
, and
Colin
Raffel
.
2021
.
mT5: A massively multilingual pre-trained text-to-text transformer
. In
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
, pages
483
498
,
Online
.
Association for Computational Linguistics
.