For sequence-to-sequence tasks it is challenging to combine individual system outputs. Further, there is also often a mismatch between the decoding criterion and the one used for assessment. Minimum Bayes' Risk (MBR) decoding can be used to combine system outputs in a manner that encourages better alignment with the final assessment criterion. This paper examines MBR decoding for Grammatical Error Correction (GEC) systems, where performance is usually evaluated in terms of edits and an associated F-score. Hence, we propose a novel MBR loss function directly linked to this form of criterion. Furthermore, an approach to expand the possible set of candidate sentences is described. This builds on a current max-voting combination scheme, as well as individual edit-level selection. Experiments on three popular GEC datasets and with state-of-the-art GEC systems demonstrate the efficacy of the proposed MBR approach. Additionally, the paper highlights how varying reward metrics within the MBR decoding framework can provide control over precision, recall, and the F-score in combined GEC systems.

翻译：对于序列到序列任务而言，合并多个独立系统的输出具有挑战性。此外，解码准则与评估准则之间常常存在不匹配。最小贝叶斯风险解码可用于合并系统输出，这种方式能促进与最终评估准则的更好对齐。本文研究了针对语法纠错系统的MBR解码，此类系统的性能通常以编辑次数及关联F值进行评估。因此，我们提出了一种与此类准则直接相关的新型MBR损失函数。此外，还描述了一种扩展候选句子集的方法，该方法基于当前的最大投票合并方案以及单独的编辑级别选择。在三个主流GEC数据集上，使用当前最先进的GEC系统进行的实验证明了所提MBR方法的有效性。本文进一步揭示了在MBR解码框架内改变奖励度量标准如何能够对集成GEC系统的精确率、召回率和F值进行控制。