Foundations and Trends® in Information Retrieval > Vol 15 > Issue 5

Deep Learning for Dialogue Systems: Chit-Chat and Beyond

By Rui Yan, Renmin University of China, China, ruiyan@ruc.edu.cn | Juntao Li, Soochow University, China, ljt@suda.edu.cn | Zhou Yu, Columbia University, USA, zy2461@columbia.edu

 
Suggested Citation
Rui Yan, Juntao Li and Zhou Yu (2022), "Deep Learning for Dialogue Systems: Chit-Chat and Beyond", Foundations and TrendsĀ® in Information Retrieval: Vol. 15: No. 5, pp 417-589. http://dx.doi.org/10.1561/1500000083

Publication Date: 16 Jun 2022
© 2022 R. Yan et al.
 
Subjects
Information retrieval
 

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In this article:
1. Introduction
2. Classic Dialogue Systems before Neural Age
3. Retrieval-Based Chit-Chat Systems
4. Generation-Based Chit-Chat Systems
5. Ensemble-Based Chit-Chat Systems
6. Connecting Chit-Chat with Tasks
7. Conclusions
Acknowledgements
References

Abstract

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.

DOI:10.1561/1500000083
ISBN: 978-1-63828-022-4
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Table of contents:
1. Introduction
2. Classic Dialogue Systems before Neural Age
3. Retrieval-Based Chit-Chat Systems
4. Generation-Based Chit-Chat Systems
5. Ensemble-Based Chit-Chat Systems
6. Connecting Chit-Chat with Tasks
7. Conclusions
Acknowledgements
References

Deep Learning for Dialogue Systems: Chit-Chat and Beyond

With the rapid progress of deep neural models and the explosion of data resources, dialogue systems that supports extensive topics and chit-chat conversations are emerging in natural language processing (NLP), information retrieval (IR), and machine learning (ML). To facilitate the development of both retrieval-based chit-chat systems and IR tasks supported by them, the authors survey chit-chat systems from two perspectives: (1) techniques to build chit-chat systems, and (2) chit-chat components in completing IR tasks.

The main contributions of this survey are: surveying the deep neural models; connecting the recently resurgent chit-chat systems and task-oriented system; introducing various solutions for challenges from different perspectives, including dataside and model-side solutions and utilization of extra resources; presenting data resources and evaluation methods for building retrieval-based and generation-based chit-chat systems. The authors also analyze the main challenges, possible new exploration directions and rising trends, which will shed light on building human-like systems.

This survey is intended to bridge the researchers of IR and the NLP community to move chit-chat systems forward and support more IR tasks. It will be of interest to IR or NLP researchers who want to study chit-chat from different perspectives, IR researchers who need to complete their tasks with the assistance of chit-chat systems, engineers with hands-on experience in building these systems to leverage advanced chit-chat modeling techniques, or anyone who wants keep up with the frontier of chit-chat systems or learn how to build them with deep neural architectures.

 
INR-083