Paraphrase Augmented Task-Oriented Dialog Generation

Understand

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.