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Authors: Jiaxu Zhao 1 ; Li Huang 1 ; Ruixuan Sun 2 ; Liao Bing 3 and Hong Qu 1

Affiliations: 1 University of Electronic Science and Technology of China, Chengdu 610054, China ; 2 Yelp Inc., U.S.A. ; 3 Chengdu Dajiangtong Technology Co., Ltd, China

Keyword(s): Neural Machine Translation, Exposure Bias, GAN.

Abstract: In recent years, Neural Machine Translation (NMT) has achieved great success, but we can not ignore two important problems. One is the exposure bias caused by the different strategies between training and inference, and the other is that the NMT model generates the best candidate word for the current step yet a bad element of the whole sentence. The popular methods to solve these two problems are Schedule Sampling and Generative Adversarial Networks (GANs) respectively, and both achieved some success. In this paper, we proposed a more precise approach called “similarity selection” combining a new GAN structure called twin-GAN to solve the above two problems. There are two generators and two discriminators in the twin-GAN. One generator uses the “similarity selection” and the other one uses the same way as inference (simulate the inference process). One discriminator guides generators at the sentence level, and the other discriminator forces these two generators to have similar distri butions. Moreover, we performed a lot of experiments on the IWSLT 2014 German→English (De→En) and the WMT’17 Chinese!English (Zh→En) and the result shows that we improved the performance compared to some other strong baseline models which based on recurrent architecture. (More)

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Paper citation in several formats:
Zhao, J.; Huang, L.; Sun, R.; Bing, L. and Qu, H. (2021). Twin-GAN for Neural Machine Translation. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 87-96. DOI: 10.5220/0010217300870096

@conference{icaart21,
author={Jiaxu Zhao. and Li Huang. and Ruixuan Sun. and Liao Bing. and Hong Qu.},
title={Twin-GAN for Neural Machine Translation},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={87-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010217300870096},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Twin-GAN for Neural Machine Translation
SN - 978-989-758-484-8
IS - 2184-433X
AU - Zhao, J.
AU - Huang, L.
AU - Sun, R.
AU - Bing, L.
AU - Qu, H.
PY - 2021
SP - 87
EP - 96
DO - 10.5220/0010217300870096
PB - SciTePress

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