@inproceedings{rahmani-etal-2023-survey,
title = "A Survey on Asking Clarification Questions Datasets in Conversational Systems",
author = "Rahmani, Hossein A. and
Wang, Xi and
Feng, Yue and
Zhang, Qiang and
Yilmaz, Emine and
Lipani, Aldo",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.152",
doi = "10.18653/v1/2023.acl-long.152",
pages = "2698--2716",
abstract = "The ability to understand a user{'}s underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Asking Clarification Questions (ACQs) to reveal users{'} true intent from their queries or utterances arise as an essential task. However, it is noticeable that a key limitation of the existing ACQs studies is their incomparability, from inconsistent use of data, distinct experimental setups and evaluation strategies. Therefore, in this paper, to assist the development of ACQs techniques, we comprehensively analyse the current ACQs research status, which offers a detailed comparison of publicly available datasets, and discusses the applied evaluation metrics, joined with benchmarks for multiple ACQs-related tasks. In particular, given a thorough analysis of the ACQs task, we discuss a number of corresponding research directions for the investigation of ACQs as well as the development of conversational systems.",
}
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<abstract>The ability to understand a user’s underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Asking Clarification Questions (ACQs) to reveal users’ true intent from their queries or utterances arise as an essential task. However, it is noticeable that a key limitation of the existing ACQs studies is their incomparability, from inconsistent use of data, distinct experimental setups and evaluation strategies. Therefore, in this paper, to assist the development of ACQs techniques, we comprehensively analyse the current ACQs research status, which offers a detailed comparison of publicly available datasets, and discusses the applied evaluation metrics, joined with benchmarks for multiple ACQs-related tasks. In particular, given a thorough analysis of the ACQs task, we discuss a number of corresponding research directions for the investigation of ACQs as well as the development of conversational systems.</abstract>
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%0 Conference Proceedings
%T A Survey on Asking Clarification Questions Datasets in Conversational Systems
%A Rahmani, Hossein A.
%A Wang, Xi
%A Feng, Yue
%A Zhang, Qiang
%A Yilmaz, Emine
%A Lipani, Aldo
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F rahmani-etal-2023-survey
%X The ability to understand a user’s underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Asking Clarification Questions (ACQs) to reveal users’ true intent from their queries or utterances arise as an essential task. However, it is noticeable that a key limitation of the existing ACQs studies is their incomparability, from inconsistent use of data, distinct experimental setups and evaluation strategies. Therefore, in this paper, to assist the development of ACQs techniques, we comprehensively analyse the current ACQs research status, which offers a detailed comparison of publicly available datasets, and discusses the applied evaluation metrics, joined with benchmarks for multiple ACQs-related tasks. In particular, given a thorough analysis of the ACQs task, we discuss a number of corresponding research directions for the investigation of ACQs as well as the development of conversational systems.
%R 10.18653/v1/2023.acl-long.152
%U https://aclanthology.org/2023.acl-long.152
%U https://doi.org/10.18653/v1/2023.acl-long.152
%P 2698-2716
Markdown (Informal)
[A Survey on Asking Clarification Questions Datasets in Conversational Systems](https://aclanthology.org/2023.acl-long.152) (Rahmani et al., ACL 2023)
ACL