论文标题
本着学习精神寻求的信息：一个关于对话好奇心的数据集
Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity
论文作者
论文摘要
开放式的人类学习和寻求信息是由数字助理越来越多地介导的。但是，这样的系统通常会忽略用户的先前知识。假设参与度与用户响应（例如“喜欢”消息或提出后续问题）之间存在相关性，我们设计了一项巫师对话框任务，该任务测试了当用户呈现与他们所知道的事实时，参与度增加的假设会增加。通过该实验的人群来源，我们收集并发布了14k对话（181k话语），用户和助手就地理主题等地理主题和地理实体和位置进行了交谈。该数据集用预先存在的用户知识，消息级对话框，接地至Wikipedia以及用户对消息的反应进行注释。使用用户的先验知识的响应增加了参与度。我们将这些知识纳入多任务模型中，该模型可以通过13个平均互惠等级点重现人类助手政策并改善BERT内容模型。
Open-ended human learning and information-seeking are increasingly mediated by digital assistants. However, such systems often ignore the user's pre-existing knowledge. Assuming a correlation between engagement and user responses such as "liking" messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know. Through crowd-sourcing of this experiment, we collect and release 14K dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages. Responses using a user's prior knowledge increase engagement. We incorporate this knowledge into a multi-task model that reproduces human assistant policies and improves over a BERT content model by 13 mean reciprocal rank points.
