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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2020,5,31]]},"abstract":"<jats:p>How to utilize information sufficiently is a key problem in neural machine translation (NMT), which is effectively improved in rich-resource NMT by leveraging large-scale bilingual sentence pairs. However, for low-resource NMT, lack of bilingual sentence pairs results in poor translation performance; therefore, taking full advantage of global information in the encoding-decoding process is effective for low-resource NMT. In this article, we propose a novel reread-feedback NMT architecture (RFNMT) for using global information. Our architecture builds upon the improved sequence-to-sequence neural network and consists of a double-deck attention-based encoder-decoder framework. In our proposed architecture, the information generated by the first-pass encoding and decoding process flows to the second-pass encoding process for more sufficient parameters initialization and information use. Specifically, we first propose a \u201creread\u201d mechanism to transfer the outputs of the first-pass encoder to the second-pass encoder, and then the output is used for the initialization of the second-pass encoder. Second, we propose a \u201cfeedback\u201d mechanism that transfers the first-pass decoder\u2019s outputs to a second-pass encoder via an important weight model and an improved gated recurrent unit (GRU). Experiments on multiple datasets show that our approach achieves significant improvements over state-of-the-art NMT systems, especially in low-resource settings.<\/jats:p>","DOI":"10.1145\/3365244","type":"journal-article","created":{"date-parts":[[2020,4,4]],"date-time":"2020-04-04T03:08:03Z","timestamp":1585969683000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Efficient Low-Resource Neural Machine Translation with Reread and Feedback Mechanism"],"prefix":"10.1145","volume":"19","author":[{"given":"Zhiqiang","family":"Yu","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4012-461X","authenticated-orcid":false,"given":"Zhengtao","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Junjun","family":"Guo","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Yuxin","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Yonghua","family":"Wen","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]}],"member":"320","published-online":{"date-parts":[[2020,1,9]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Mikel Artetxe Gorka Labaka Eneko Agirre and Kyunghyun Cho. 2017. 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