@inproceedings{aliannejadi-etal-2021-building,
title = "Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions",
author = "Aliannejadi, Mohammad and
Kiseleva, Julia and
Chuklin, Aleksandr and
Dalton, Jeff and
Burtsev, Mikhail",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.367/",
doi = "10.18653/v1/2021.emnlp-main.367",
pages = "4473--4484",
abstract = "Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of {\textquoteleft}asking clarifying questions in open-domain dialogues': (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research."
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<abstract>Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of ‘asking clarifying questions in open-domain dialogues’: (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.</abstract>
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%0 Conference Proceedings
%T Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions
%A Aliannejadi, Mohammad
%A Kiseleva, Julia
%A Chuklin, Aleksandr
%A Dalton, Jeff
%A Burtsev, Mikhail
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F aliannejadi-etal-2021-building
%X Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of ‘asking clarifying questions in open-domain dialogues’: (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.
%R 10.18653/v1/2021.emnlp-main.367
%U https://aclanthology.org/2021.emnlp-main.367/
%U https://doi.org/10.18653/v1/2021.emnlp-main.367
%P 4473-4484
Markdown (Informal)
[Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions](https://aclanthology.org/2021.emnlp-main.367/) (Aliannejadi et al., EMNLP 2021)
ACL