Paraphrase Augmented Task-Oriented Dialog Generation

Silin Gao, Yichi Zhang, Zhijian Ou, Zhou Yu


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.
Anthology ID:
2020.acl-main.60
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
639–649
Language:
URL:
https://aclanthology.org/2020.acl-main.60
DOI:
10.18653/v1/2020.acl-main.60
Bibkey:
Cite (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.
Cite (Informal):
Paraphrase Augmented Task-Oriented Dialog Generation (Gao et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.60.pdf
Video:
 http://slideslive.com/38928976
Code
 Silin159/PARG
Data
MultiWOZ