When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?
Abstract
Word alignment has proven to benefit many-to-many neural machine translation (NMT). However, high-quality ground-truth bilingual dictionaries were used for pre-editing in previous methods, which are unavailable for most language pairs. Meanwhile, the contrastive objective can implicitly utilize automatically learned word alignment, which has not been explored in many-to-many NMT. This work proposes a word-level contrastive objective to leverage word alignments for many-to-many NMT. Empirical results show that this leads to 0.8 BLEU gains for several language pairs. Analyses reveal that in many-to-many NMT, the encoder's sentence retrieval performance highly correlates with the translation quality, which explains when the proposed method impacts translation. This motivates future exploration for many-to-many NMT to improve the encoder's sentence retrieval performance.
Keywords
Cite
@article{arxiv.2204.12165,
title = {When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?},
author = {Zhuoyuan Mao and Chenhui Chu and Raj Dabre and Haiyue Song and Zhen Wan and Sadao Kurohashi},
journal= {arXiv preprint arXiv:2204.12165},
year = {2022}
}
Comments
NAACL 2022 findings