论文标题
基准数据和评估框架，以发现Covid-19疫苗犹豫不决
Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy
论文作者
论文摘要
Covid-19-大流行造成了巨大的全球影响，并造成了数百万的生命。随着COVID-19疫苗推出，它们很快就会毫不犹豫地遇到。为了解决犹豫不决的人的关注，我们启动了Vira，这是一个公共对话系统，旨在解决COVID-19疫苗周围的问题和问题。在这里，我们发布了Viradialogs，这是一个由Vira实际用户进行的超过8K对话的数据集，提供了独特的现实对话数据集。鉴于用户意图的快速变化，鉴于准则的更新或响应新信息，我们重点介绍了此用例中意图发现的重要任务。我们介绍了一个新型的自动评估框架，以发现意图发现，利用现有的Vira意图分类器。我们使用此框架来报告基线意图发现结果，这突出了该任务的困难。
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.
