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arXiv · 2204.12165

When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?

Zhuoyuan Mao·Chenhui Chu·Raj Dabre·Haiyue Song·Zhen Wan·Sadao Kurohashi
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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.

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Zhuoyuan Mao, Chenhui Chu, Raj Dabre, Haiyue Song, Zhen Wan, Sadao Kurohashi. 2022-04-26. When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?. https://arxiv.org/abs/2204.12165

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