A Unified Dialogue User Simulator for Few-shot Data Augmentation
Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang · 2022
Pre-trained language models have shown superior performance in task-oriented dialogues.However, existing datasets are on limited scales, which cannot support large-scale pre-training.Fortunately, various data augmentation methods have been developed to augment largescale task-oriented dialogue corpora.However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work.In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets.The model can then be tuned on a target domain with fewshot data.The experiments on a target dataset across multiple domains show that our proposed model brings remarkable performance increases through data augmentation.Goal: You want to book a taxi.The taxi should go to anatolia and should leave after 22:00.The taxi should depart from kymmoy.Make sure you get car type and contact number.