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Bilingual attention based neural machine translation

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Abstract

In recent years, Recurrent Neural Network based Neural Machine Translation (RNN-based NMT) equipped with an attention mechanism from the decoder to encoder, has achieved great advancements and exhibited good performance in many language pairs. However, little work has been done on the attention mechanism for the target side, which has the potential to further improve NMT. To address this issue, in this paper, we propose a novel bilingual attention based NMT, where its bilingual attention mechanism exploits decoding history and enables the NMT model to better dynamically select and exploit source side and target side information. Compared with previous RNN-based NMT models, our model has two advantages: First, our model exercises a dynamic control over the ratios at which source and target contexts respectively contribute to the generation of the next target word. In this way, the weakly induced structure relations on both sides can be exploited for NMT. Second, through short-cut connections, the training errors of our model can be directly back-propagated, which effectively alleviates the gradient vanishing or exploding issue. Experimental results and in-depth analyses on Chinese-English, English-German, and English-French translation tasks show that our model with proper configurations can significantly surpass the dominant NMT model, Transformer. Particularly, our proposed model has won the first prize in the English-Chinese translation task of WMT2018.

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Notes

  1. The corpora include LDC2002E18, LDC2003E07, LDC2003E14, Hansards portion of LDC2004T07, LDC2004T08 and LDC2005T06.

  2. https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/mu\lti-bleu.perl

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Acknowledgements

The authors was supported by National Key Research and Development Program of China (No. 2020AAA0108004), National Natural Science Foundation of China (No. 61672440), Natural Science Foundation of Fujian Province of China (No.2020J06001), Youth Innovation Fund of Xiamen (Grant No. 3502Z20206059), and Industry-University-Research Project of Xiamen City (3502Z20203002). Fei Long and Jinsong Su are corresponding authors. We also thank the anonymous reviewers for their insightful comments.

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Authors and Affiliations

  1. Center for Digital Media Computing and Software Engineering, Xiamen University, Xiamen, People’s Republic of China

    Liyan Kang, Shaojie He, Fei Long & Jinsong Su

  2. Mobile Internet Group, Tencent Technology Co., Ltd, Beijing, People’s Republic of China

    Mingxuan Wang

  3. Institute of Artificial Intelligence, Xiamen University, Xiamen, People’s Republic of China

    Jinsong Su

  4. Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, Ministry of Culture and Tourism, Xiamen, People’s Republic of China

    Jinsong Su

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  1. Liyan Kang
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Correspondence to Fei Long.

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Kang, L., He, S., Wang, M. et al. Bilingual attention based neural machine translation. Appl Intell 53, 4302–4315 (2023). https://doi.org/10.1007/s10489-022-03563-8

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