@inproceedings{li-etal-2019-decomposable,
title = "Decomposable Neural Paraphrase Generation",
author = "Li, Zichao and
Jiang, Xin and
Shang, Lifeng and
Liu, Qun",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
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/P19-1332",
doi = "10.18653/v1/P19-1332",
pages = "3403--3414",
abstract = "Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence at different levels of granularity in a disentangled way. Specifically, the model is composed of multiple encoders and decoders with different structures, each of which corresponds to a specific granularity. The empirical study shows that the decomposition mechanism of DNPG makes paraphrase generation more interpretable and controllable. Based on DNPG, we further develop an unsupervised domain adaptation method for paraphrase generation. Experimental results show that the proposed model achieves competitive in-domain performance compared to state-of-the-art neural models, and significantly better performance when adapting to a new domain.",
}
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<abstract>Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence at different levels of granularity in a disentangled way. Specifically, the model is composed of multiple encoders and decoders with different structures, each of which corresponds to a specific granularity. The empirical study shows that the decomposition mechanism of DNPG makes paraphrase generation more interpretable and controllable. Based on DNPG, we further develop an unsupervised domain adaptation method for paraphrase generation. Experimental results show that the proposed model achieves competitive in-domain performance compared to state-of-the-art neural models, and significantly better performance when adapting to a new domain.</abstract>
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%0 Conference Proceedings
%T Decomposable Neural Paraphrase Generation
%A Li, Zichao
%A Jiang, Xin
%A Shang, Lifeng
%A Liu, Qun
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F li-etal-2019-decomposable
%X Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence at different levels of granularity in a disentangled way. Specifically, the model is composed of multiple encoders and decoders with different structures, each of which corresponds to a specific granularity. The empirical study shows that the decomposition mechanism of DNPG makes paraphrase generation more interpretable and controllable. Based on DNPG, we further develop an unsupervised domain adaptation method for paraphrase generation. Experimental results show that the proposed model achieves competitive in-domain performance compared to state-of-the-art neural models, and significantly better performance when adapting to a new domain.
%R 10.18653/v1/P19-1332
%U https://aclanthology.org/P19-1332
%U https://doi.org/10.18653/v1/P19-1332
%P 3403-3414
Markdown (Informal)
[Decomposable Neural Paraphrase Generation](https://aclanthology.org/P19-1332) (Li et al., ACL 2019)
ACL
- Zichao Li, Xin Jiang, Lifeng Shang, and Qun Liu. 2019. Decomposable Neural Paraphrase Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3403–3414, Florence, Italy. Association for Computational Linguistics.