Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding (2024.naacl-short)
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| Challenge: | Recent work shows that MBR decoding can significantly improve translation performance of Multilingual Large Language Models. |
| Approach: | They propose a method that uses a monolingual fine-tuning set to fine- tune MLLMs to get the gains of MBR without additional computation in inference. |
| Outcome: | The proposed method outperforms greedy decoding and beam search on multiple NMT tests. |
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