Non-autoregressive translation (NAT) models are typically trained with the
cross-entropy loss, which forces the model outputs to be aligned verbatim with
the target sentence and will highly penalize small shifts in word positions.
Latent alignment models relax the explicit alignment by marginalizing out all
monotonic latent alignments with the CTC loss. However, they cannot handle
non-monotonic alignments, which is non-negligible as there is typically global
word reordering in machine translation. In this work, we explore non-monotonic
latent alignments for NAT. We extend the alignment space to non-monotonic
alignments to allow for the global word reordering and further consider all
alignments that overlap with the target sentence. We non-monotonically match
the alignments to the target sentence and train the latent alignment model to
maximize the F1 score of non-monotonic matching. Extensive experiments on major
WMT benchmarks show that our method substantially improves the translation
performance of CTC-based models. Our best model achieves 30.06 BLEU on WMT14
En-De with only one-iteration decoding, closing the gap between
non-autoregressive and autoregressive models.

本文提出了一种利用非单调潜在对齐的方法，扩展非自回归翻译模型的非单调对齐空间，进一步考虑与目标句子重叠的所有对齐，并训练潜在对齐模型以最大化非单调匹配的 F1 分数，从而提高 CTC-based 模型的翻译性能，最佳模型在 WMT14 En-De 上实现了 30.06 BLEU 的性能表现。