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Word-Pair Relevance Modeling with Multi-View Neural Attention Mechanism for Sentence Alignment
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作者 Ying Ding Jun-Hui Li +1 位作者 Zheng-Xian Gong Guo-Dong Zhou 《Journal of Computer Science & Technology》 SCIE EI CSCD 2020年第3期617-628,共12页
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... 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. 展开更多
关键词 sentence alignment neural network word-pair relevance network multi-view attention network
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