A unified dialogue user simulator for few-shot data augmentation

• Main Authors: WAN, Dazhen, ZHANG, Zheng, ZHU, Qi, LIAO, Lizi, HUANG, Minlie
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Computational linguistics; Augmentation methods; Data augmentation; Pre-training; Artificial Intelligence and Robotics; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/7719; https://ink.library.smu.edu.sg/context/sis_research/article/8722/viewcontent/A_unified_dialogue_user_simulator_for_few_shot_data_augmentation.pdf
• Institution: Singapore Management University

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.