1. Paper title

Paraphrase Augmented Task-Oriented Dialog Generation

2. link

https://www.aclweb.org/anthology/2020.acl-main.60.pdf

3. 摘要

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 significantly outperforms other data augmentation methods in dialog generation tasks, especially under low resource settings. 1 2

[?] paraphrase model
[?] dialog state
[?] dialog act labels

4. 要解决什么问题

dialog generation依赖大量的高质量数据。

5. 作者的主要贡献

a paraphrase augmented response generation (PARG) framework
把paraphrase model结合到response generation model中
基于dialog state和dialog act labels自动生成paraphrase训练集

6. 得到了什么结果

CamRest676和MultiWOZ上性能优于STOA。
PARG也优于其它数据增强技术。

7. 关键字

数据增强