Dialogue State Induction Using Neural Latent Variable Models
- School of Engineering, Westlake University（西湖大学工学院）
- Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖高等研究院前沿技术研究所）
- Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology（哈尔滨工业大学社会计算与信息检索研究中心）
- School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。
英文摘要：
Dialogue state modules are a useful component in a task-oriented dialogue system. Traditional methods find dialogue states by manually labeling training corpora, upon which neural models are trained. However, the labeling process can be costly, slow, error-prone, and more importantly, cannot cover the vast range of domains in real-world dialogues for customer service. We propose the task of dialogue state induction, building two neural latent variable models that mine dialogue states automatically from unlabeled customer service dialogue records. Results show that the models can effectively find meaningful slots. In addition, equipped with induced dialogue states, a state-of-the-art dialogue system gives better performance compared with not using a dialogue state module.
