Foundations and Trends® in Information Retrieval > Vol 13 > Issue 2-3

Neural Approaches to Conversational AI

Jianfeng Gao, Microsoft Research, USA, jfgao@microsoft.com Michel Galley, Microsoft, USA, mgalley@microsoft.com Lihong Li, Google Brain, USA, lihong@google.com
 
Suggested Citation
Jianfeng Gao, Michel Galley and Lihong Li (2019), "Neural Approaches to Conversational AI", Foundations and TrendsĀ® in Information Retrieval: Vol. 13: No. 2-3, pp 127-298. http://dx.doi.org/10.1561/1500000074

Published: 21 Feb 2019
© 2019 J. Gao, M. Galley and L. Li
 
Subjects
Question answering,  Text mining,  Assistive technologies,  Interdisciplinary influence :Artificial intelligence and the user interface,  Assistive technologies,  Interdisciplinary influence :Artificial intelligence and the user interface,  Deep Learning,  Reinforcement learning,  Human-Robot Interaction:Dialog Systems
 

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In this article:
1. Introduction
2. Machine Learning Background
3. Question Answering and Machine Reading Comprehension
4. Task-oriented Dialogue Systems
5. Fully Data-Driven Conversation Models and Social Bots
6. Conversational AI in Industry
7. Conclusions and Research Trends
Acknowledgements
References

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.

DOI:10.1561/1500000074
ISBN: 978-1-68083-552-6
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ISBN: 978-1-68083-553-3
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Table of contents:
1. Introduction
2. Machine Learning Background
3. Question Answering and Machine Reading Comprehension
4. Task-oriented Dialogue Systems
5. Fully Data-Driven Conversation Models and Social Bots
6. Conversational AI in Industry
7. Conclusions and Research Trends
Acknowledgements
References

Neural Approaches to Conversational AI: Question Answering, Task-oriented Dialogues and Social Chatbots

This monograph is the first survey of neural approaches to conversational AI that targets Natural Language Processing and Information Retrieval audiences. It provides a comprehensive survey of the neural approaches to conversational AI that have been developed in the last few years, covering QA, task-oriented and social bots with a unified view of optimal decision making.

The authors draw connections between modern neural approaches and traditional approaches, allowing readers to better understand why and how the research has evolved and to shed light on how they can move forward. They also present state-of-the-art approaches to training dialogue agents using both supervised and reinforcement learning. Finally, the authors sketch out the landscape of conversational systems developed in the research community and released in industry, demonstrating via case studies the progress that has been made and the challenges that are still being faced.

Neural Approaches to Conversational AI is a valuable resource for students, researchers, and software developers. It provides a unified view, as well as a detailed presentation of the important ideas and insights needed to understand and create modern dialogue agents that will be instrumental to making world knowledge and services accessible to millions of users in ways that seem natural and intuitive.

 
INR-074