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Alleviating repetitive tokens in non-autoregressive machine translation with unlikelihood training

  • Neural Networks
  • Published:
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Abstract

In recent years, significant progress has been made in the field of non-autoregressive machine translations. However, the accuracy of non-autoregressive models still lags behind their autoregressive counterparts. This discrepancy can be attributed to the abundance of repetitive tokens in the target sequences generated by non-autoregressive models. In this study, we delve into this phenomenon and propose a novel approach to train a non-autoregressive model using unlikelihood loss. We evaluate our method on three widely used benchmark tasks. The experimental results demonstrating that our proposed approach significantly reduces the number of repetitive tokens while improving the overall performance of non-autoregressive machine translations. Compared to the baseline model ”Mask-Predict”, the average number of repetitions on IWSLT 14 DE\(\rightarrow \)EN valid set is reduced from 0.48 to 0.17, resulting in a remarkable 62% decrease.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61732005, 61671064).

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 61732005, 61671064)

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Authors and Affiliations

  1. School of Computer and Software, Nanyang Institute of Technology, Nanyang, China

    Shuheng Wang

  2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Shumin Shi & Heyan Huang

Authors
  1. Shuheng Wang
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  2. Shumin Shi
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  3. Heyan Huang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Shuheng Wang. The first draft of the manuscript was written by Shuheng Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shumin Shi.

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Wang, S., Shi, S. & Huang, H. Alleviating repetitive tokens in non-autoregressive machine translation with unlikelihood training. Soft Comput 28, 4681–4688 (2024). https://doi.org/10.1007/s00500-023-09490-1

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