Title: Minimum Bayes-Risk Decoding for Statistical Machine Translation Authors: Shankar Kumar, William Byrne Published: 2004-01-01 Link: https://aclanthology.org/N04-1022.pdf
Abstract
We present Minimum Bayes-Risk (MBR) decoding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance. We describe a hierarchy of loss functions that incorporate different levels of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences. We report the performance of the MBR decoders on a Chinese-to-English translation task. Our results show that MBR decoding can be used to tune statistical MT performance for specific loss functions.