论文标题
ViratrustData：关于COVID-19疫苗的人类茶几对话的信任宣布的语料库
VIRATrustData: A Trust-Annotated Corpus of Human-Chatbot Conversations About COVID-19 Vaccines
论文作者
论文摘要
公众对医疗信息的信任对于成功应用公共卫生政策，例如疫苗的摄取至关重要。当聊天机器人远程提供信息时，尤其如此，这些信息近年来变得越来越受欢迎。在这里，我们探讨了人类机器人转交信任分类的具有挑战性的任务。我们依靠最近发布的与Covid-19疫苗信息资源助理Vira Chatbot的观察收集（而不是众包）对话的数据。这些对话围绕着关于Covid-19-19疫苗的问题和担忧，在这种问题中，信任特别严重。我们注释了$ 3K $ VIRA System-User对话转弯，用于低机构信任或低代理信托与中立或高信托。我们发布了标记的数据集Viratrustdata，这是我们所知的最好的。我们演示了该任务是非平凡的，并比较了几个预测不同信任水平的模型。
Public trust in medical information is crucial for successful application of public health policies such as vaccine uptake. This is especially true when the information is offered remotely, by chatbots, which have become increasingly popular in recent years. Here, we explore the challenging task of human-bot turn-level trust classification. We rely on a recently released data of observationally-collected (rather than crowdsourced) dialogs with VIRA chatbot, a COVID-19 Vaccine Information Resource Assistant. These dialogs are centered around questions and concerns about COVID-19 vaccines, where trust is particularly acute. We annotated $3k$ VIRA system-user conversational turns for Low Institutional Trust or Low Agent Trust vs. Neutral or High Trust. We release the labeled dataset, VIRATrustData, the first of its kind to the best of our knowledge. We demonstrate how this task is non-trivial and compare several models that predict the different levels of trust.
