Word-Pair Relevance Modeling with Multi-View Neural Attention Mechanism for Sentence Alignment

Sentence alignment provides multi-lingual or cross-lingual natural language processing (NLP) applications with high-quality parallel sentence pairs. Normally, an aligned sentence pair contains multiple aligned words, which intuitively play different roles during sentence alignment. Inspired by this intuition, we propose to deal with the problem of sentence alignment by exploring the semantic interactionship among fine-grained word pairs within the framework of neural network. In particular, we first employ various relevance measures to capture various kinds of semantic interactions among word pairs by using a word-pair relevance network, and then model their importance by using a multi-view attention network. Experimental results on both monotonic and non-monotonic bitexts show that our proposed approach significantly improves the performance of sentence alignment.