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.

中文翻译：

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用于句子对齐的多视图神经注意机制的词对相关性建模

句子对齐为多语言或跨语言自然语言处理 (NLP) 应用程序提供高质量的平行句对。通常，一个对齐的句对包含多个对齐的词，它们在句子对齐过程中直观地扮演着不同的角色。受这种直觉的启发，我们建议通过在神经网络框架内探索细粒度词对之间的语义交互来处理句子对齐问题。特别是，我们首先通过使用词对相关网络采用各种相关性度量来捕获词对之间的各种语义交互，然后使用多视图注意力网络对其重要性进行建模。