In plain words: This survey reviews recent deep-learning methods—systems that learn patterns from data—for three kinds of talk: question answering, task-focused assistants, and open chat. It connects each to older hand-built approaches, showing how far each has come and what still fails.
Abstract
The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.
Jianfeng Gao, Michel Galley, Lihong Li
arXiv:1809.08267 · cs.CL · submitted Sep 21, 2018 · updated Sep 10, 2019
abstract · pdf · html · Foundations and Trends in Information Retrieval (95 pages)