Neural Approaches to Conversational AI
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Neural Approaches to Conversational AI Question Answering, Task-oriented Dialogues and Social Chatbots Other titles in Foundations and Trends R in Information Retrieval Web Forum Retrieval and Text Analytics: A Survey Doris Hoogeveen, Li Wang, Timothy Baldwin and Karin M. Verspoor ISBN: 978-1-68083-350-8 Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting Jun Wang, Weinan Zhang and Shuai Yuan ISBN: 978-1-68083-310-2 Applications of Topic Models Jordan Boyd-Graber, Yuening Hu and David Mimno ISBN: 978-1-68083-308-9 Searching the Enterprise Udo Kruschwitz and Charlie Hull ISBN: 978-1-68083-304-1 Neural Approaches to Conversational AI Question Answering, Task-oriented Dialogues and Social Chatbots Jianfeng Gao Microsoft Research [email protected] Michel Galley Microsoft Research [email protected] Lihong Li Google Brain [email protected] Boston — Delft Foundations and Trends R in Information Retrieval Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 United States Tel. +1-781-985-4510 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 The preferred citation for this publication is J. Gao, M. Galley and L. Li. Neural Approaches to Conversational AI. Foundations and Trends R in Information Retrieval, vol. 13, no. 2-3, pp. 127–298, 2019. ISBN: 978-1-68083-553-3 c 2019 J. Gao, M. Galley and L. Li All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Photocopying. In the USA: This journal is registered at the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923. 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Please apply to now Publishers, PO Box 179, 2600 AD Delft, The Netherlands, www.nowpublishers.com; e-mail: [email protected] Foundations and Trends R in Information Retrieval Volume 13, Issue 2-3, 2019 Editorial Board Editors-in-Chief Maarten de Rijke Yiqun Liu University of Amsterdam Tsinghua University The Netherlands China Editors Ben Carterette Jian-Yun Nie University of Delaware Université de Montreal Charles L.A. Clarke Jimmy Lin University of Waterloo University of Maryland Claudia Hauff Leif Azzopardi Delft University of Technology University of Glasgow Diane Kelly Lynda Tamine University of Tennessee University of Toulouse Doug Oard Mark D. Smucker University of Maryland University of Waterloo Ellen M. Voorhees Rodrygo Luis Teodoro Santos National Institute of Standards and Universidade Federal de Minas Gerais Technology Ryen White Fabrizio Sebastiani Microsoft Research Consiglio Nazionale delle Ricerche, Italy Shane Culpepper Hang Li RMIT Bytedance Technology Soumen Chakrabarti Ian Ruthven Indian Institute of Technology University of Strathclyde, Glasgow Tie-Yan Liu Jaap Kamps Microsoft Research University of Amsterdam James Allan University of Massachusetts, Amherst Editorial Scope Topics Foundations and Trends R in Information Retrieval publishes survey and tutorial articles in the following topics: • Applications of IR • Metasearch, rank aggregation and data fusion • Architectures for IR • Collaborative filtering and • Natural language processing for recommender systems IR • Cross-lingual and multilingual • Performance issues for IR IR systems, including algorithms, data structures, optimization • Distributed IR and federated techniques, and scalability search • Question answering • Evaluation issues and test collections for IR • Summarization of single documents, multiple • Formal models and language documents, and corpora models for IR • Text mining • IR on mobile platforms • Indexing and retrieval of • Topic detection and tracking structured documents • Usability, interactivity, and • Information categorization and visualization issues in IR clustering • User modelling and user • Information extraction studies for IR • Information filtering and • Web search routing Information for Librarians Foundations and Trends R in Information Retrieval, 2019, Volume 13, 5 issues. ISSN paper version 1554-0669. ISSN online version 1554-0677. Also available as a combined paper and online subscription. Contents 1 Introduction2 1.1 Who Should Read this Paper? ............... 4 1.2 Dialogue: What Kinds of Problems? ............ 5 1.3 A Unified View: Dialogue as Optimal Decision Making .. 7 1.4 The Transition of NLP to Neural Approaches ....... 9 2 Machine Learning Background 13 2.1 Machine Learning Basics .................. 13 2.2 Deep Learning ........................ 17 2.3 Reinforcement Learning ................... 23 3 Question Answering and Machine Reading Comprehension 29 3.1 Knowledge Base ....................... 30 3.2 Semantic Parsing for KB-QA ................ 31 3.3 Embedding-based Methods ................. 32 3.4 Multi-Step Reasoning on KB ................ 34 3.5 Conversational KB-QA Agents ............... 40 3.6 Machine Reading for Text-QA ............... 44 3.7 Neural MRC Models ..................... 47 3.8 Conversational Text-QA Agents ............... 53 4 Task-oriented Dialogue Systems 56 4.1 Overview........................... 57 4.2 Evaluation and User Simulation ............... 62 4.3 Natural Language Understanding and Dialogue State Tracking 68 4.4 Dialogue Policy Learning .................. 73 4.5 Natural Language Generation ................ 83 4.6 End-to-end Learning ..................... 86 4.7 Further Remarks ....................... 88 5 Fully Data-Driven Conversation Models and Social Bots 90 5.1 End-to-End Conversation Models .............. 91 5.2 Challenges and Remedies .................. 95 5.3 Grounded Conversation Models ............... 103 5.4 Beyond Supervised Learning ................. 105 5.5 Data ............................. 107 5.6 Evaluation .......................... 109 5.7 Open Benchmarks ...................... 112 6 Conversational AI in Industry 114 6.1 Question Answering Systems ................ 114 6.2 Task-oriented Dialogue Systems (Virtual Assistants) .... 118 6.3 Chatbots ........................... 121 7 Conclusions and Research Trends 124 Acknowledgements 128 References 129 Neural Approaches to Conversational AI Jianfeng Gao1, Michel Galley2 and Lihong Li3 1Microsoft Research; [email protected] 2Microsoft Research; [email protected] 3Google Brain; [email protected] ABSTRACT The present paper surveys neural approaches to conversa- tional 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 and Lihong Li (2019), “Neural Approaches to Conversa- tional AI”, Foundations and Trends R in Information Retrieval: Vol. 13, No. 2-3, pp 127–298. DOI: 10.1561/1500000074. 1 Introduction Developing an intelligent dialogue system1 that not only emulates human conversation, but also answers questions on topics ranging from latest news about a movie star to Einstein’s theory of relativity, and fulfills complex tasks such as travel planning, has been one of the longest running goals in AI. The goal has remained elusive until recently. We are now observing promising results both in academia sindustry, as large amounts of conversational data become available for training, and the breakthroughs in deep learning (DL) and reinforcement learning (RL) are applied to conversational AI. Conversational AI is fundamental to natural user interfaces. It is a rapidly growing field, attracting many researchers in the Natural Language Processing (NLP), Information Retrieval (IR) and Machine Learning (ML) communities. For example, SIGIR 2018 has created a new track of Artificial Intelligence, Semantics, and Dialog to bridge research in AI and IR, especially targeting Question Answering (QA), deep semantics and dialogue with intelligent agents. 1“Dialogue systems” and “conversational AI” are often used interchangeably in the scientific literature. The difference is reflective of different traditions. The former term is more general in that a dialogue system might be purely rule-based rather than AI-based. 2 3 Recent years have seen the rise of a small industry of tutorials and survey papers on deep