Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information (2022.naacl-main)
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| Challenge: | Recent studies show that Neural Machine Translation models struggle to disambiguate polysemous words without lapsing into their most frequent senses. |
| Approach: | They propose a way to automatically create high-precision sense-annotated parallel corpora . they then propose 'fine-tuning' strategies to exploit these sense annotations during training . |
| Outcome: | The proposed approach achieves higher BLEU scores than its vanilla counterpart in 3 language pairs. |
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