@inproceedings{gretz-etal-2023-benchmark,
title = "Benchmark Data and Evaluation Framework for Intent Discovery Around {COVID}-19 Vaccine Hesitancy",
author = "Gretz, Shai and
Toledo, Assaf and
Friedman, Roni and
Lahav, Dan and
Weeks, Rose and
Bar-Zeev, Naor and
Sedoc, Jo{\~a}o and
Sangha, Pooja and
Katz, Yoav and
Slonim, Noam",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.100",
doi = "10.18653/v1/2023.findings-eacl.100",
pages = "1358--1370",
abstract = "The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-19 vaccines. Here, we release VIRADialogs, a dataset of over 8k dialogues conducted by actual users with VIRA, providing a unique real-world conversational dataset. In light of rapid changes in users{'} intents, due to updates in guidelines or in response to new information, we highlight the important task of intent discovery in this use-case. We introduce a novel automatic evaluation framework for intent discovery, leveraging the existing intent classifier of VIRA. We use this framework to report baseline intent discovery results over VIRADialogs, that highlight the difficulty of this task.",
}
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<abstract>The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-19 vaccines. Here, we release VIRADialogs, a dataset of over 8k dialogues conducted by actual users with VIRA, providing a unique real-world conversational dataset. In light of rapid changes in users’ intents, due to updates in guidelines or in response to new information, we highlight the important task of intent discovery in this use-case. We introduce a novel automatic evaluation framework for intent discovery, leveraging the existing intent classifier of VIRA. We use this framework to report baseline intent discovery results over VIRADialogs, that highlight the difficulty of this task.</abstract>
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%0 Conference Proceedings
%T Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy
%A Gretz, Shai
%A Toledo, Assaf
%A Friedman, Roni
%A Lahav, Dan
%A Weeks, Rose
%A Bar-Zeev, Naor
%A Sedoc, João
%A Sangha, Pooja
%A Katz, Yoav
%A Slonim, Noam
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F gretz-etal-2023-benchmark
%X The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-19 vaccines. Here, we release VIRADialogs, a dataset of over 8k dialogues conducted by actual users with VIRA, providing a unique real-world conversational dataset. In light of rapid changes in users’ intents, due to updates in guidelines or in response to new information, we highlight the important task of intent discovery in this use-case. We introduce a novel automatic evaluation framework for intent discovery, leveraging the existing intent classifier of VIRA. We use this framework to report baseline intent discovery results over VIRADialogs, that highlight the difficulty of this task.
%R 10.18653/v1/2023.findings-eacl.100
%U https://aclanthology.org/2023.findings-eacl.100
%U https://doi.org/10.18653/v1/2023.findings-eacl.100
%P 1358-1370
Markdown (Informal)
[Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy](https://aclanthology.org/2023.findings-eacl.100) (Gretz et al., Findings 2023)
ACL
- Shai Gretz, Assaf Toledo, Roni Friedman, Dan Lahav, Rose Weeks, Naor Bar-Zeev, João Sedoc, Pooja Sangha, Yoav Katz, and Noam Slonim. 2023. Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1358–1370, Dubrovnik, Croatia. Association for Computational Linguistics.