@inproceedings{gao-etal-2020-paraphrase,
title = "Paraphrase Augmented Task-Oriented Dialog Generation",
author = "Gao, Silin and
Zhang, Yichi and
Ou, Zhijian and
Yu, Zhou",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.60",
doi = "10.18653/v1/2020.acl-main.60",
pages = "639--649",
abstract = "Neural generative models have achieved promising performance on dialog generation tasks if given a huge data set. However, the lack of high-quality dialog data and the expensive data annotation process greatly limit their application in real world settings. We propose a paraphrase augmented response generation (PARG) framework that jointly trains a paraphrase model and a response generation model to improve the dialog generation performance. We also design a method to automatically construct paraphrase training data set based on dialog state and dialog act labels. PARG is applicable to various dialog generation models, such as TSCP (Lei et al., 2018) and DAMD (Zhang et al., 2019). Experimental results show that the proposed framework improves these state-of-the-art dialog models further on CamRest676 and MultiWOZ. PARG also outperforms other data augmentation methods significantly in dialog generation tasks, especially under low resource settings.",
}
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<abstract>Neural generative models have achieved promising performance on dialog generation tasks if given a huge data set. However, the lack of high-quality dialog data and the expensive data annotation process greatly limit their application in real world settings. We propose a paraphrase augmented response generation (PARG) framework that jointly trains a paraphrase model and a response generation model to improve the dialog generation performance. We also design a method to automatically construct paraphrase training data set based on dialog state and dialog act labels. PARG is applicable to various dialog generation models, such as TSCP (Lei et al., 2018) and DAMD (Zhang et al., 2019). Experimental results show that the proposed framework improves these state-of-the-art dialog models further on CamRest676 and MultiWOZ. PARG also outperforms other data augmentation methods significantly in dialog generation tasks, especially under low resource settings.</abstract>
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%0 Conference Proceedings
%T Paraphrase Augmented Task-Oriented Dialog Generation
%A Gao, Silin
%A Zhang, Yichi
%A Ou, Zhijian
%A Yu, Zhou
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F gao-etal-2020-paraphrase
%X Neural generative models have achieved promising performance on dialog generation tasks if given a huge data set. However, the lack of high-quality dialog data and the expensive data annotation process greatly limit their application in real world settings. We propose a paraphrase augmented response generation (PARG) framework that jointly trains a paraphrase model and a response generation model to improve the dialog generation performance. We also design a method to automatically construct paraphrase training data set based on dialog state and dialog act labels. PARG is applicable to various dialog generation models, such as TSCP (Lei et al., 2018) and DAMD (Zhang et al., 2019). Experimental results show that the proposed framework improves these state-of-the-art dialog models further on CamRest676 and MultiWOZ. PARG also outperforms other data augmentation methods significantly in dialog generation tasks, especially under low resource settings.
%R 10.18653/v1/2020.acl-main.60
%U https://aclanthology.org/2020.acl-main.60
%U https://doi.org/10.18653/v1/2020.acl-main.60
%P 639-649
Markdown (Informal)
[Paraphrase Augmented Task-Oriented Dialog Generation](https://aclanthology.org/2020.acl-main.60) (Gao et al., ACL 2020)
ACL
- Silin Gao, Yichi Zhang, Zhijian Ou, and Zhou Yu. 2020. Paraphrase Augmented Task-Oriented Dialog Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 639–649, Online. Association for Computational Linguistics.