Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation models are usually trained with word-level loss under teacher forcing algorithm . however, this method suffers from exposure bias due to high variance of gradient estimation . |
| Approach: | They propose a method with a differentiable sequence-level training objective . they use greedy search to alleviate the problem of exposure bias . |
| Outcome: | Experiments on Chinese-to-English translation tasks show that the proposed method outperforms the reinforcement-based methods. |
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On Search Strategies for Document-Level Neural Machine Translation (2023.findings-acl)
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