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

Minimum Bayes' Risk Decoding for System Combination of Grammatical Error Correction Systems