A Survey on Response Selection for Retrieval-based Dialogues
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4619-4626.
https://doi.org/10.24963/ijcai.2021/627
Keywords
- Natural language processing: General
Abstract
Building an intelligent dialogue system capable of naturally and coherently conversing with humans has been a long-standing goal of artificial intelligence. In the past decade, with the development of machine/deep learning technology and the explosive growth of available conversation data in social media, numerous neural models have been developed for context-response matching tasks in retrieval-based dialogue systems, with more fluent and informative responses compared with generative models. This paper presents a comprehensive survey of recent advances in response selection for retrieval-based dialogues. In particular, we first formulate the problem of response selection and review state-of-the-art context-response matching models categorized by their architecture.
Then we summarize some recent advances on the research of response selection, including incorporation with extra knowledge and exploration on more effective model learning. Finally, we highlight the challenges which are not yet well addressed in this task and present future research directions.
@inproceedings{ijcai2021p627,
title = {A Survey on Response Selection for Retrieval-based Dialogues},
author = {Tao, Chongyang and Feng, Jiazhan and Yan, Rui and Wu, Wei and Jiang, Daxin},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {4619--4626},
year = {2021},
month = {8},
note = {Survey Track},
doi = {10.24963/ijcai.2021/627},
url = {https://doi.org/10.24963/ijcai.2021/627},
}
TY - CPAPER TI - A Survey on Response Selection for Retrieval-based Dialogues AU - Tao, Chongyang AU - Feng, Jiazhan AU - Yan, Rui AU - Wu, Wei AU - Jiang, Daxin T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 4619 EP - 4626 DO - 10.24963/ijcai.2021/627 UR - https://doi.org/10.24963/ijcai.2021/627 ER -Copied
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