Skip Nav Destination
Close Modal
Update search
NARROW
Format
Journal
Date
Availability
1-2 of 2
Dayiheng Liu
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Journal Articles
Publisher: Journals Gateway
Computational Linguistics (2024) 50 (1): 25–47.
Published: 01 March 2024
FIGURES
| View All (4)
Abstract
View article
PDF
The utilization of monolingual data has been shown to be a promising strategy for addressing low-resource machine translation problems. Previous studies have demonstrated the effectiveness of techniques such as back-translation and self-supervised objectives, including masked language modeling, causal language modeling, and denoise autoencoding, in improving the performance of machine translation models. However, the manner in which these methods contribute to the success of machine translation tasks and how they can be effectively combined remains an under-researched area. In this study, we carry out a systematic investigation of the effects of these techniques on linguistic properties through the use of probing tasks, including source language comprehension, bilingual word alignment, and translation fluency. We further evaluate the impact of pre-training, back-translation, and multi-task learning on bitexts of varying sizes. Our findings inform the design of more effective pipelines for leveraging monolingual data in extremely low-resource and low-resource machine translation tasks. Experiment results show consistent performance gains in seven translation directions, which provide further support for our conclusions and understanding of the role of monolingual data in machine translation.
Journal Articles
Publisher: Journals Gateway
Computational Linguistics (2022) 48 (4): 887–906.
Published: 01 December 2022
FIGURES
| View All (5)
Abstract
View article
PDF
Query language identification (Q-LID) plays a crucial role in a cross-lingual search engine. There exist two main challenges in Q-LID: (1) insufficient contextual information in queries for disambiguation; and (2) the lack of query-style training examples for low-resource languages. In this article, we propose a neural Q-LID model by alleviating the above problems from both model architecture and data augmentation perspectives. Concretely, we build our model upon the advanced Transformer model. In order to enhance the discrimination of queries, a variety of external features (e.g., character, word, as well as script) are fed into the model and fused by a multi-scale attention mechanism. Moreover, to remedy the low resource challenge in this task, a novel machine translation–based strategy is proposed to automatically generate synthetic query-style data for low-resource languages. We contribute the first Q-LID test set called QID-21 , which consists of search queries in 21 languages. Experimental results reveal that our model yields better classification accuracy than strong baselines and existing LID systems on both query and traditional LID tasks. 1