Phrase information has been successfully integrated into current state-of-the-art neural machine translation (NMT) models. However, the natural property of the source and target phrase alignment has not been explored. In this paper, we propose a novel phrase-level agreement method to deal with this problem. First, we explore n-gram models over minimal translation units (MTUs) to explicitly capture aligned bilingual phrases from the parallel corpora. Then, we propose a phrase-level agreement loss that directly reduces the difference between the representations of the source-side and target-side phrase. Finally, we integrate the phrase-level agreement loss into the NMT models, to improve the translation performance. Empirical results on the NIST Chinese-to-English and the WMT English-to-German translation tasks demonstrate that the proposed phrase-level agreement method achieves significant improvements over state-of-the-art baselines, demonstrating the effectiveness and necessity of exploiting phrase-level agreement for NMT.
CITATION STYLE
Yang, M., Wang, X., Zhang, M., & Zhao, T. (2020). Incorporating Phrase-Level Agreement into Neural Machine Translation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12430 LNAI, pp. 416–428). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60450-9_33
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