Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation (2021.acl-long)
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| Challenge: | Prior work treats all types of mismatches between source and target as noise . Consequently, it remains unclear how noisy parallel training samples impact NMT training. |
| Approach: | They propose a divergent-aware NMT framework that uses factors to help NMT recover from the degradation caused by naturally occurring divergences. |
| Outcome: | The proposed framework improves translation quality and model calibration on EN-FR tasks. |
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Revisiting Robust Neural Machine Translation: A Transformer Case Study (2021.findings-emnlp)
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| Challenge: | Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora. |
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Tagged Back-translation Revisited: Why Does It Really Work? (2020.acl-main)
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| Challenge: | In this paper, we show that neural machine translation systems trained on large back-translated data overfit some of the characteristics of machine-transcribed texts. |
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| Challenge: | Existing studies show that transfer learning works best when the languages are related. |
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Multilingual Neural Machine Translation (2020.coling-tutorials)
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| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
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Depth Growing for Neural Machine Translation (P19-1)
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