Neural Approaches to Conversational AI

被引：153

作者：

Gao, Jianfeng ^{[1
]}

Galley, Michel ^{[1
]}

Li, Lihong ^{[2
]}

机构：

[1] Microsoft Res, Redmond, WA 98052 USA

[2] Google Brain, Montreal, PQ, Canada

来源：

FOUNDATIONS AND TRENDS IN INFORMATION RETRIEVAL | 2019年 / 13卷 / 2-3期

关键词：

SPOKEN DIALOGUE MANAGEMENT; USER SIMULATION; PROBABILISTIC FRAMEWORK; LANGUAGE GENERATION; DECISION-PROCESSES; INFINITE-HORIZON; POLICY; NETWORKS; SYSTEMS; MODEL;

D O I：

10.1561/1500000074

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：127 / 298

页数：172

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