Prevent the Language Model from being Overconfident in Neural Machine Translation (2021.acl-long)
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| Challenge: | Neural Machine Translation models are based on partial translation and a language model that predicts the next token based only on partial. |
| Approach: | They propose a Margin-based Token-level Objective and a Sentence-level Goal to maximize the Margin . they propose to model the next token based on partial translation . |
| Outcome: | The proposed approach improves translation adequacy and fluency on English-to-German, Chinese-to English and French translation tasks. |
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| Challenge: | Neural Machine Translation (NMT) has made remarkable progress over the past years, but under-translation and over-translatation remain challenging obstacles faced by NMT systems. |
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| Challenge: | Statistical MT decomposes the translation task into distinct components that are learned separately. |
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| Challenge: | Empirical results show that a sentence-level agreement module can significantly improve the performance of neural machine translation (NMT) |
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| Challenge: | Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, attention may fail to capture word alignment for some NMT models. |
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| Challenge: | Neural Machine Translation (NMT) models are used to solve translation problems using long-term models. |
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On the Importance of Word Boundaries in Character-level Neural Machine Translation (D19-56)
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