A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation

Wenqing Chen, Jidong Tian, Liqiang Xiao, Hao He, Yaohui Jin

Abstract
Paraphrase generation aims to generate semantically consistent sentences with different syntactic realizations. Most of the recent studies rely on the typical encoder-decoder framework where the generation process is deterministic. However, in practice, the ability to generate multiple syntactically different paraphrases is important. Recent work proposed to cooperate variational inference on a target-related latent variable to introduce the diversity. But the latent variable may be contaminated by the semantic information of other unrelated sentences, and in turn, change the conveyed meaning of generated paraphrases. In this paper, we propose a semantically consistent and syntactically variational encoder-decoder framework, which uses adversarial learning to ensure the syntactic latent variable be semantic-free. Moreover, we adopt another discriminator to improve the word-level and sentence-level semantic consistency. So the proposed framework can generate multiple semantically consistent and syntactically different paraphrases. The experiments show that our model outperforms the baseline models on the metrics based on both n-gram matching and semantic similarity, and our model can generate multiple different paraphrases by assembling different syntactic variables.
Anthology ID:
2020.coling-main.102
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1186–1198
Language:
URL:
https://aclanthology.org/2020.coling-main.102
DOI:
10.18653/v1/2020.coling-main.102
Bibkey:
Cite (ACL):
Wenqing Chen, Jidong Tian, Liqiang Xiao, Hao He, and Yaohui Jin. 2020. A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1186–1198, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation (Chen et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.102.pdf
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