Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy
EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023
; : 1328-1340, 2023.
Artigo
em Inglês
| Scopus | ID: covidwho-20236251
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. © 2023 Association for Computational Linguistics.
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Coleções:
Bases de dados de organismos internacionais
Base de dados:
Scopus
Tipo de estudo:
Estudo experimental
Tópicos:
Vacinas
Idioma:
Inglês
Revista:
EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023
Ano de publicação:
2023
Tipo de documento:
Artigo
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