Generating Sentences from Disentangled Syntactic and Semantic Spaces

Yu Bao, Hao Zhou, Shujian Huang, Lei Li, Lili Mou, Olga Vechtomova, Xin-yu Dai, Jiajun Chen

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Abstract
Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. However, generating sentences from the continuous latent space does not explicitly model the syntactic information. In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces. Our proposed method explicitly models syntactic information in the VAE’s latent space by using the linearized tree sequence, leading to better performance of language generation. Additionally, the advantage of sampling in the disentangled syntactic and semantic latent spaces enables us to perform novel applications, such as the unsupervised paraphrase generation and syntax transfer generation. Experimental results show that our proposed model achieves similar or better performance in various tasks, compared with state-of-the-art related work.
Anthology ID:
P19-1602
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6008–6019
Language:
URL:
https://aclanthology.org/P19-1602
DOI:
10.18653/v1/P19-1602
Bibkey:
Cite (ACL):
Yu Bao, Hao Zhou, Shujian Huang, Lei Li, Lili Mou, Olga Vechtomova, Xin-yu Dai, and Jiajun Chen. 2019. Generating Sentences from Disentangled Syntactic and Semantic Spaces. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6008–6019, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Generating Sentences from Disentangled Syntactic and Semantic Spaces (Bao et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/P19-1602.pdf
Code
 baoy-nlp/DSS-VAE
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