Unveiling the Power of Source: Source-based Minimum Bayes Risk Decoding for Neural Machine Translation (2025.acl-long)
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| Challenge: | Maximum a posteriori decoding aims to maximize the estimated posterior probability, but high estimated probability does not always lead to high translation quality. |
| Approach: | They propose a method that seeks hypotheses with the highest expected utility by using quasi-sources as “support hypothese . they propose sMBR decoding which utilizes a reference-free quality estimation metric as the utility function. |
| Outcome: | The proposed approach outperforms QE reranking and the standard MBR decoding. |
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| Challenge: | Minimum Bayes Risk (MBR) decoding is a decision rule used to generate sequences from autoregressive probability models (e.g., LLMs). |
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| Challenge: | Minimum Bayes risk (MBR) decoding is a decision rule of text generation . however, it depends on sample texts drawn from the text generation model . |
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| Challenge: | Recent advances in machine translation (MT) have shown that minimum bayes risk decoding can be a powerful alternative to beam search. |
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| Challenge: | Minimum Bayes-Risk (MBR) decoding is a powerful alternative to beam search for text generation tasks. |
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