Recent advances in deep learning based dialogue systems: a systematic survey
Dialogue systems are a popular natural language processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learnin...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/170372 |
| _version_ | 1826122353876140032 |
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| author | Ni, Jinjie Young, Tom Pandelea, Vlad Xue, Fuzhao Cambria, Erik |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Ni, Jinjie Young, Tom Pandelea, Vlad Xue, Fuzhao Cambria, Erik |
| author_sort | Ni, Jinjie |
| collection | NTU |
| description | Dialogue systems are a popular natural language processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learning based due to their outstanding performance. In this survey, we mainly focus on the deep learning based dialogue systems. We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. Specifically, from the angle of model type, we discuss the principles, characteristics, and applications of different models that are widely used in dialogue systems. This will help researchers acquaint these models and see how they are applied in state-of-the-art frameworks, which is rather helpful when designing a new dialogue system. From the angle of system type, we discuss task-oriented and open-domain dialogue systems as two streams of research, providing insight into the hot topics related. Furthermore, we comprehensively review the evaluation methods and datasets for dialogue systems to pave the way for future research. Finally, some possible research trends are identified based on the recent research outcomes. To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present for deep learning based dialogue systems, extensively covering the popular techniques. We speculate that this work is a good starting point for academics who are new to the dialogue systems or those who want to quickly grasp up-to-date techniques in this area. |
| first_indexed | 2024-10-01T05:47:08Z |
| format | Journal Article |
| id | ntu-10356/170372 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:47:08Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1703722023-09-11T01:20:52Z Recent advances in deep learning based dialogue systems: a systematic survey Ni, Jinjie Young, Tom Pandelea, Vlad Xue, Fuzhao Cambria, Erik School of Computer Science and Engineering Engineering::Computer science and engineering Dialogue Systems Chatbots Dialogue systems are a popular natural language processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learning based due to their outstanding performance. In this survey, we mainly focus on the deep learning based dialogue systems. We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. Specifically, from the angle of model type, we discuss the principles, characteristics, and applications of different models that are widely used in dialogue systems. This will help researchers acquaint these models and see how they are applied in state-of-the-art frameworks, which is rather helpful when designing a new dialogue system. From the angle of system type, we discuss task-oriented and open-domain dialogue systems as two streams of research, providing insight into the hot topics related. Furthermore, we comprehensively review the evaluation methods and datasets for dialogue systems to pave the way for future research. Finally, some possible research trends are identified based on the recent research outcomes. To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present for deep learning based dialogue systems, extensively covering the popular techniques. We speculate that this work is a good starting point for academics who are new to the dialogue systems or those who want to quickly grasp up-to-date techniques in this area. Agency for Science, Technology and Research (A*STAR) This research/project is supported by A*STAR under its Industry Alignment Fund (LOA Award I1901E0046). 2023-09-11T01:20:52Z 2023-09-11T01:20:52Z 2023 Journal Article Ni, J., Young, T., Pandelea, V., Xue, F. & Cambria, E. (2023). Recent advances in deep learning based dialogue systems: a systematic survey. Artificial Intelligence Review, 56(4), 3055-3155. https://dx.doi.org/10.1007/s10462-022-10248-8 0269-2821 https://hdl.handle.net/10356/170372 10.1007/s10462-022-10248-8 2-s2.0-85136985671 4 56 3055 3155 en I1901E0046 Artificial Intelligence Review © 2022 The Author(s), under exclusive licence to Springer Nature B.V. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Dialogue Systems Chatbots Ni, Jinjie Young, Tom Pandelea, Vlad Xue, Fuzhao Cambria, Erik Recent advances in deep learning based dialogue systems: a systematic survey |
| title | Recent advances in deep learning based dialogue systems: a systematic survey |
| title_full | Recent advances in deep learning based dialogue systems: a systematic survey |
| title_fullStr | Recent advances in deep learning based dialogue systems: a systematic survey |
| title_full_unstemmed | Recent advances in deep learning based dialogue systems: a systematic survey |
| title_short | Recent advances in deep learning based dialogue systems: a systematic survey |
| title_sort | recent advances in deep learning based dialogue systems a systematic survey |
| topic | Engineering::Computer science and engineering Dialogue Systems Chatbots |
| url | https://hdl.handle.net/10356/170372 |
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