通过约束一致性的方法，提升概率MBR解码的翻译质量与效率。
Agreement-Constrained Probabilistic Minimum Bayes Risk Decoding
- 用知识蒸馏模型指导得分矩阵补全，减少评估次数。
- 在WMT'23英德翻译任务上，误差降低3倍，质量更优。
- 适合需要高效高质翻译的场景，如实时机器翻译系统。
最小贝叶斯风险（MBR）解码通过最大化输出候选的期望效用生成高质量翻译，但需对候选集内所有词对进行评分，时间复杂度为候选数的平方。为减少效用函数调用次数，概率性MBR（PMBR）解码采用采样词对并用矩阵补全算法完成缺失得分，但会降低翻译质量。为此，本文提出约束一致性的概率MBR（AC-PMBR）解码，利用知识蒸馏模型引导得分矩阵补全。在WMT'23英德翻译任务上，该方法使矩阵补全的近似误差最多降低3倍，在相近计算成本下实现了比PMBR更高的翻译质量。
原文摘要 · Abstract (English)
Minimum Bayes risk (MBR) decoding generates high-quality translations by maximizing the expected utility of output candidates, but it evaluates all pairwise scores over the candidate set; hence, it takes quadratic time with respect to the number of candidates. To reduce the number of utility function calls, probabilistic MBR (PMBR) decoding partially evaluates quality scores using sampled pairs of candidates and completes the missing scores with a matrix completion algorithm. Nevertheless, it degrades the translation quality as the number of utility function calls is reduced. Therefore, to improve the trade-off between quality and cost, we propose agreement-constrained PMBR (AC-PMBR) decoding, which leverages a knowledge distilled model to guide the completion of the score matrix. Our AC-PMBR decoding improved approximation errors of matrix completion by up to 3 times and achieved higher translation quality compared with PMBR decoding at a comparable computational cost on the WMT'23 En$\leftrightarrow$De translation tasks.
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