Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions
Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, Mikhail Burtsev
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.
BibTeX
@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"
}