@inproceedings{yang-etal-2019-end,
title = "An End-to-End Generative Architecture for Paraphrase Generation",
author = "Yang, Qian and
Huo, Zhouyuan and
Shen, Dinghan and
Cheng, Yong and
Wang, Wenlin and
Wang, Guoyin and
Carin, Lawrence",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1309/",
doi = "10.18653/v1/D19-1309",
pages = "3132--3142",
abstract = "Generating high-quality paraphrases is a fundamental yet challenging natural language processing task. Despite the effectiveness of previous work based on generative models, there remain problems with exposure bias in recurrent neural networks, and often a failure to generate realistic sentences. To overcome these challenges, we propose the first end-to-end conditional generative architecture for generating paraphrases via adversarial training, which does not depend on extra linguistic information. Extensive experiments on four public datasets demonstrate the proposed method achieves state-of-the-art results, outperforming previous generative architectures on both automatic metrics (BLEU, METEOR, and TER) and human evaluations."
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%0 Conference Proceedings
%T An End-to-End Generative Architecture for Paraphrase Generation
%A Yang, Qian
%A Huo, Zhouyuan
%A Shen, Dinghan
%A Cheng, Yong
%A Wang, Wenlin
%A Wang, Guoyin
%A Carin, Lawrence
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F yang-etal-2019-end
%X Generating high-quality paraphrases is a fundamental yet challenging natural language processing task. Despite the effectiveness of previous work based on generative models, there remain problems with exposure bias in recurrent neural networks, and often a failure to generate realistic sentences. To overcome these challenges, we propose the first end-to-end conditional generative architecture for generating paraphrases via adversarial training, which does not depend on extra linguistic information. Extensive experiments on four public datasets demonstrate the proposed method achieves state-of-the-art results, outperforming previous generative architectures on both automatic metrics (BLEU, METEOR, and TER) and human evaluations.
%R 10.18653/v1/D19-1309
%U https://aclanthology.org/D19-1309/
%U https://doi.org/10.18653/v1/D19-1309
%P 3132-3142
Markdown (Informal)
[An End-to-End Generative Architecture for Paraphrase Generation](https://aclanthology.org/D19-1309/) (Yang et al., EMNLP-IJCNLP 2019)
ACL
- Qian Yang, Zhouyuan Huo, Dinghan Shen, Yong Cheng, Wenlin Wang, Guoyin Wang, and Lawrence Carin. 2019. An End-to-End Generative Architecture for Paraphrase Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3132–3142, Hong Kong, China. Association for Computational Linguistics.