Exploring Diverse Expressions for Paraphrase Generation

Lihua Qian, Lin Qiu, Weinan Zhang, Xin Jiang, Yong Yu


Abstract
Paraphrasing plays an important role in various natural language processing (NLP) tasks, such as question answering, information retrieval and sentence simplification. Recently, neural generative models have shown promising results in paraphrase generation. However, prior work mainly focused on single paraphrase generation, while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. Few works have been done to solve diverse paraphrase generation. In this paper, we propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. A reinforcement learning algorithm is applied to train our model. Our experiments on two real-world datasets demonstrate that our model not only gains a significant increase in diversity but also improves generation quality over several state-of-the-art baselines.
Anthology ID:
D19-1313
Volume:
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:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3173–3182
Language:
URL:
https://aclanthology.org/D19-1313
DOI:
10.18653/v1/D19-1313
Bibkey:
Cite (ACL):
Lihua Qian, Lin Qiu, Weinan Zhang, Xin Jiang, and Yong Yu. 2019. Exploring Diverse Expressions 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 3173–3182, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Exploring Diverse Expressions for Paraphrase Generation (Qian et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1313.pdf