Deep Learning for Dialogue Systems: Chit-Chat and Beyond
Rui Yan, Juntao Li, Yu Zhou · Foundations and Trends® in Information Retrieval · 2022
With the rapid progress of deep neural models and the explosion of available data resources, dialogue systems that supports extensive topics and chit-chat conversations are emerging as a research hot-spot for many communities, e.g., information retrieval (IR), natural language processing (NLP), and machine learning (ML). Building a chit-chat system with retrieval techniques is an essential task and has achieved great success in the past few years. The advance of chit-chat systems, in turn, can support extensive IR tasks, e.g., conversational search and conversational recommendation. To facilitate the development of both retrieval-based chit-chat systems and IR tasks supported by these systems, we survey chit-chat systems from two perspectives: (1) techniques to build chit-chat systems, i.e., deep retrieval-based models, generative methods, and their ensembles, and (2) chit-chat components in completing IR tasks. In each aspect, we present cutting-edge neural methods and summarize the core challenges encountered and possible research directions.