@inproceedings{bao-etal-2019-generating,
title = "Generating Sentences from Disentangled Syntactic and Semantic Spaces",
author = "Bao, Yu and
Zhou, Hao and
Huang, Shujian and
Li, Lei and
Mou, Lili and
Vechtomova, Olga and
Dai, Xin-yu and
Chen, Jiajun",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1602",
doi = "10.18653/v1/P19-1602",
pages = "6008--6019",
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.",
}
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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.</abstract>
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%0 Conference Proceedings
%T Generating Sentences from Disentangled Syntactic and Semantic Spaces
%A Bao, Yu
%A Zhou, Hao
%A Huang, Shujian
%A Li, Lei
%A Mou, Lili
%A Vechtomova, Olga
%A Dai, Xin-yu
%A Chen, Jiajun
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 jul
%I Association for Computational Linguistics
%C Florence, Italy
%F bao-etal-2019-generating
%X 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.
%R 10.18653/v1/P19-1602
%U https://aclanthology.org/P19-1602
%U https://doi.org/10.18653/v1/P19-1602
%P 6008-6019
Markdown (Informal)
[Generating Sentences from Disentangled Syntactic and Semantic Spaces](https://aclanthology.org/P19-1602) (Bao et al., ACL 2019)
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.