Natural Language Processing Research

Volume 1, Issue 3-4, March 2021, Pages 56 - 70

Neural Dialogue Generation Methods in Open Domain: A Survey

Authors
Bin Sun, Kan Li*, ORCID
School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China
*Corresponding author. Email: likan@bit.edu.cn
Corresponding Author
Kan Li
Received 7 December 2020, Accepted 17 February 2021, Available Online 19 March 2021.
DOI
https://doi.org/10.2991/nlpr.d.210223.001How to use a DOI?
Keywords
Neural dialogue generation, Open-domain dialogue system, Natural language processing
Abstract

Open-Domain Dialogue Generation (human–computer interaction) is an important issue in the field of Natural Language Processing (NLP). Because of the improvement of deep learning techniques, a large number of neural dialogue generative methods were proposed to generate better responses. In this survey, we elaborated the research history of these existing generative methods, and then roughly divided them into six categories, i.e., Encoder-Decoder framework-based methods, Hierarchical Recurrent Encoder-Decoder (HRED)-based methods, Variational Autoencoder (VAE)-based methods, Reinforcement Learning (RL)- based methods, Generative Adversarial Network (GAN)-based methods, and pretraining-model-based methods. We dived into the methods of each category and gave the detailed discussions of these methods. After that, we presented a comparison among the different categories of methods and analyzed their advantages and disadvantages. We enumerated some open access public datasets and some commonly used automatic evaluating metrics. Finally, we discuss some possible research directions that can take the research of neural dialogue generation into a new frontier in the future.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Natural Language Processing Research
Volume-Issue
1 - 3-4
Pages
56 - 70
Publication Date
2021/03/19
ISSN (Online)
2666-0512
DOI
https://doi.org/10.2991/nlpr.d.210223.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Bin Sun
AU  - Kan Li
PY  - 2021
DA  - 2021/03/19
TI  - Neural Dialogue Generation Methods in Open Domain: A Survey
JO  - Natural Language Processing Research
SP  - 56
EP  - 70
VL  - 1
IS  - 3-4
SN  - 2666-0512
UR  - https://doi.org/10.2991/nlpr.d.210223.001
DO  - https://doi.org/10.2991/nlpr.d.210223.001
ID  - Sun2021
ER  -