Centroid-Based Efficient Minimum Bayes Risk Decoding
Hiroyuki Deguchi, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe, Hideki Tanaka, Masao Utiyama
Abstract
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 5.7 times, but also outperformed vanilla MBR decoding in translation quality by up to 0.5 COMET in the WMT’22 En↔Ja, En↔De, En↔Zh, and WMT’23 En↔Ja translation tasks.
BibTeX
@inproceedings{deguchi-etal-2024-centroid,
title = "Centroid-Based Efficient Minimum {B}ayes Risk Decoding",
author = "Deguchi, Hiroyuki and
Sakai, Yusuke and
Kamigaito, Hidetaka and
Watanabe, Taro and
Tanaka, Hideki and
Utiyama, Masao",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.654/",
doi = "10.18653/v1/2024.findings-acl.654",
pages = "11009--11018"
}