@inproceedings{cai-etal-2022-learning,
title = "Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition",
author = "Cai, Pengshan and
Wan, Hui and
Liu, Fei and
Yu, Mo and
Yu, Hong and
Joshi, Sachindra",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.352",
doi = "10.18653/v1/2022.naacl-main.352",
pages = "4781--4796",
abstract = "We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. Our information acquisition-oriented dialogue system employs a novel adaptation of reinforced self-play so that the system can be transferred to various domains without in-domain dialogue data, and can carry out conversations both informative and attentive to users.",
}
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%0 Conference Proceedings
%T Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition
%A Cai, Pengshan
%A Wan, Hui
%A Liu, Fei
%A Yu, Mo
%A Yu, Hong
%A Joshi, Sachindra
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F cai-etal-2022-learning
%X We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. Our information acquisition-oriented dialogue system employs a novel adaptation of reinforced self-play so that the system can be transferred to various domains without in-domain dialogue data, and can carry out conversations both informative and attentive to users.
%R 10.18653/v1/2022.naacl-main.352
%U https://aclanthology.org/2022.naacl-main.352
%U https://doi.org/10.18653/v1/2022.naacl-main.352
%P 4781-4796
Markdown (Informal)
[Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition](https://aclanthology.org/2022.naacl-main.352) (Cai et al., NAACL 2022)
ACL