论文标题
对比度对准何时会改善多到许多神经机器的翻译？
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?
论文作者
论文摘要
事实证明，单词一致性可以使多到多的神经机器翻译（NMT）受益。但是，在以前的方法中，使用高质量的基础双语词典进行预编辑，这对于大多数语言对而言是不可用的。同时，对比目标可以隐式地使用自动学习的单词对齐，这在许多一对多的NMT中尚未探讨。这项工作提出了一个单词级的对比目标，以利用多到许多NMT的单词对齐方式。经验结果表明，这将导致几对语言对的0.8 BLEU增长。分析表明，在多对多的NMT中，编码器的句子检索与翻译质量高度相关，这解释了何时拟议的方法会影响翻译。这激发了许多对许多NMT的探索，以改善编码器的句子检索表现。
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.
