论文标题
释义增强以任务为导向的对话框
Paraphrase Augmented Task-Oriented Dialog Generation
论文作者
论文摘要
如果给出了庞大的数据集，则神经生成模型在对话生成任务上实现了有希望的性能。但是，缺乏高质量的对话数据和昂贵的数据注释过程极大地限制了它们在现实世界中的应用。我们提出了一个释义增强响应生成（PARG）框架，该框架共同训练释义模型和响应生成模型，以提高对话框的生成性能。我们还设计一种方法，可以根据对话状态和对话框标签自动构建释义训练数据集。 PARG适用于各种对话生成模型，例如TSCP（Lei等，2018）和Damd（Zhang等，2019）。实验结果表明，所提出的框架在CAMREST676和MULTIWOZ上进一步改进了这些最先进的对话模型。 PARG还显着优于对话生成任务中的其他数据增强方法，尤其是在低资源设置下。
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.
