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Unpaired Multimodal Neural Machine Translation via Reinforcement Learning

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Database Systems for Advanced Applications (DASFAA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12682))

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Abstract

End-to-end neural machine translation (NMT) heavily relies on parallel corpora for training. However, high-quality parallel corpora are usually costly to collect. To tackle this problem, multimodal content, especially image, has been introduced to help build an NMT system without parallel corpora. In this paper, we propose a reinforcement learning (RL) method to build an NMT system by introducing a sequence-level supervision signal as a reward. Based on the fact that visual information can be a universal representation to ground different languages, we design two different rewards to guide the learning process, i.e., (1) the likelihood of generated sentence given source image and (2) the distance of attention weights given by image caption models. Experimental results on the Multi30K, IAPR-TC12, and IKEA datasets show that the proposed learning mechanism achieves better performance than existing methods.

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Notes

  1. 1.

    http://blog.flickr.net/.

  2. 2.

    https://mobile.twitter.com/.

  3. 3.

    https://github.com/tensorflow/tensor2tensor.

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Acknowledgement

This research was partially supported by grants from the National Key Research and Development Program of China (No. 2016YFB1000904) and the National Natural Science Foundation of China (Nos. 61727809, 61922073 and U20A20229).

Author information

Authors and Affiliations

  1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei, China

    Yijun Wang, Qi Liu & Enhong Chen

  2. Department of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Tianxin Wei

Authors
  1. Yijun Wang
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  2. Tianxin Wei
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  3. Qi Liu
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  4. Enhong Chen
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Corresponding author

Correspondence to Enhong Chen .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  4. The Pennsylvania State University, University Park, PA, USA

    Wang-Chien Lee

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  6. Athens University of Economics and Business, Athens, Greece

    Vana Kalogeraki

  7. National Cheng Kung University, Tainan City, Taiwan

    Jen-Wei Huang

  8. National Tsing Hua University, Hsinchu, Taiwan

    Chih-Ya Shen

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Wang, Y., Wei, T., Liu, Q., Chen, E. (2021). Unpaired Multimodal Neural Machine Translation via Reinforcement Learning. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12682. Springer, Cham. https://doi.org/10.1007/978-3-030-73197-7_11

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  • DOI: https://doi.org/10.1007/978-3-030-73197-7_11

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