@inproceedings{xu-etal-2018-unpaired,
title = "Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach",
author = "Xu, Jingjing and
Sun, Xu and
Zeng, Qi and
Zhang, Xiaodong and
Ren, Xuancheng and
Wang, Houfeng and
Li, Wenjie",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1090",
doi = "10.18653/v1/P18-1090",
pages = "979--988",
abstract = "The goal of sentiment-to-sentiment {``}translation{''} is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To solve this problem, we propose a cycled reinforcement learning method that enables training on unpaired data by collaboration between a neutralization module and an emotionalization module. We evaluate our approach on two review datasets, Yelp and Amazon. Experimental results show that our approach significantly outperforms the state-of-the-art systems. Especially, the proposed method substantially improves the content preservation performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to 14.06 on the two datasets, respectively.",
}
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<abstract>The goal of sentiment-to-sentiment “translation” is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To solve this problem, we propose a cycled reinforcement learning method that enables training on unpaired data by collaboration between a neutralization module and an emotionalization module. We evaluate our approach on two review datasets, Yelp and Amazon. Experimental results show that our approach significantly outperforms the state-of-the-art systems. Especially, the proposed method substantially improves the content preservation performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to 14.06 on the two datasets, respectively.</abstract>
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%0 Conference Proceedings
%T Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
%A Xu, Jingjing
%A Sun, Xu
%A Zeng, Qi
%A Zhang, Xiaodong
%A Ren, Xuancheng
%A Wang, Houfeng
%A Li, Wenjie
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F xu-etal-2018-unpaired
%X The goal of sentiment-to-sentiment “translation” is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To solve this problem, we propose a cycled reinforcement learning method that enables training on unpaired data by collaboration between a neutralization module and an emotionalization module. We evaluate our approach on two review datasets, Yelp and Amazon. Experimental results show that our approach significantly outperforms the state-of-the-art systems. Especially, the proposed method substantially improves the content preservation performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to 14.06 on the two datasets, respectively.
%R 10.18653/v1/P18-1090
%U https://aclanthology.org/P18-1090
%U https://doi.org/10.18653/v1/P18-1090
%P 979-988
Markdown (Informal)
[Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach](https://aclanthology.org/P18-1090) (Xu et al., ACL 2018)
ACL