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.

对语法错误纠正系统中的最小贝叶斯风险编码进行研究，提出了与此形式标准直接相关的新型 MBR 损失函数，并描述了一种扩展可能候选句集合的方法，实验证明了该 MBR 方法的有效性和对精确率、召回率和综合 GEC 系统的 F 分数的控制能力。