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Tobias Schnabel
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Journal Articles
Publisher: Journals Gateway
Transactions of the Association for Computational Linguistics (2022) 10: 163–177.
Published: 09 February 2022
FIGURES
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In the summarization domain, a key requirement for summaries is to be factually consistent with the input document. Previous work has found that natural language inference (NLI) models do not perform competitively when applied to inconsistency detection. In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level). We provide a highly effective and light-weight method called SummaC Conv that enables NLI models to be successfully used for this task by segmenting documents into sentence units and aggregating scores between pairs of sentences. We furthermore introduce a new benchmark called SummaC ( Summa ry C onsistency) which consists of six large inconsistency detection datasets. On this dataset, SummaC Conv obtains state-of-the-art results with a balanced accuracy of 74.4%, a 5% improvement compared with prior work.
Journal Articles
Publisher: Journals Gateway
Transactions of the Association for Computational Linguistics (2014) 2: 15–26.
Published: 01 February 2014
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We present FLORS, a new part-of-speech tagger for domain adaptation. FLORS uses robust representations that work especially well for unknown words and for known words with unseen tags. FLORS is simpler and faster than previous domain adaptation methods, yet it has significantly better accuracy than several baselines.