@inproceedings{8e2d46e0aab546c3be0effa31aeea917,
title = "Neural machine translation with phrasal attention",
abstract = "Attention-based neural machine translation (NMT) employs an attention network to capture structural correspondences between the source and target language at the word level. Unfortunately, alignments between source and target equivalents are complicated, which makes word-level attention not adequate to model these relations (e.g., alignments between a source idiom and its target translation). In order to handle this issue, we propose a phrase-level attention mechanism to complement the word-level attention network in this paper. The proposed phrasal attention framework is simple yet effective, keeping the strength of phrase-based statistical machine translation (SMT) on the source side. Experiments on Chinese-to-English translation task demonstrate that the proposed method is able to statistically improve word-level attention-based NMT.",
keywords = "Attention mechaism, Gated recurrent unit, Neural machine translation, Recurrent neural network",
author = "Yachao Li and Deyi Xiong and Min Zhang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; 13th China Workshop on Machine Translation, CWMT 2017 ; Conference date: 27-09-2017 Through 29-09-2017",
year = "2017",
doi = "10.1007/978-981-10-7134-8\_1",
language = "英语",
isbn = "9789811071331",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "1--8",
editor = "Wong, \{Derek F.\} and Deyi Xiong",
booktitle = "Machine Translation - 13th China Workshop, CWMT 2017, Revised Selected Papers",
address = "德国",
}