Paraphrase Generation with Deep Reinforcement Learning

Zichao Li, Xin Jiang, Lifeng Shang, Hang Li

Abstract
Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP). In this paper, we present a deep reinforcement learning approach to paraphrase generation. Specifically, we propose a new framework for the task, which consists of a generator and an evaluator, both of which are learned from data. The generator, built as a sequence-to-sequence learning model, can produce paraphrases given a sentence. The evaluator, constructed as a deep matching model, can judge whether two sentences are paraphrases of each other. The generator is first trained by deep learning and then further fine-tuned by reinforcement learning in which the reward is given by the evaluator. For the learning of the evaluator, we propose two methods based on supervised learning and inverse reinforcement learning respectively, depending on the type of available training data. Experimental results on two datasets demonstrate the proposed models (the generators) can produce more accurate paraphrases and outperform the state-of-the-art methods in paraphrase generation in both automatic evaluation and human evaluation.
Anthology ID:
D18-1421
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3865–3878
Language:
URL:
https://aclanthology.org/D18-1421
DOI:
10.18653/v1/D18-1421
Bibkey:
Cite (ACL):
Zichao Li, Xin Jiang, Lifeng Shang, and Hang Li. 2018. Paraphrase Generation with Deep Reinforcement Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3865–3878, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Paraphrase Generation with Deep Reinforcement Learning (Li et al., EMNLP 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/improve-issue-templates/D18-1421.pdf
PDF Search