Centroid-Based Efficient Minimum Bayes Risk Decoding
Hiroyuki Deguchi Yusuke Sakai Hidetaka Kamigaito Taro Watanabe Hideki Tanaka Masao Utiyama
Minimum Bayes risk (MBR) decoding achieved state-of-the-art translation
performance by using COMET, a neural metric that has a high correlation with
human evaluation. However, MBR decoding requires quadratic time since it
computes the expected score between a translation hypothesis and all reference
translations. We propose centroid-based MBR (CBMBR) decoding to improve the
speed of MBR decoding. Our method clusters the reference translations in the
feature space, and then calculates the score using the centroids of each
cluster. The experimental results show that our CBMBR not only improved the
decoding speed of the expected score calculation 6.9 times, but also
outperformed vanilla MBR decoding in translation quality by up to 0.5 COMET in
the WMT'22 En$\leftrightarrow$Ja, En$\leftrightarrow$De, En$\leftrightarrow$Zh,
and WMT'23 En$\leftrightarrow$Ja translation tasks.