  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.
