ConvSearch: A Open-Domain Conversational Search Behavior Dataset

Conversational Search has been paid much attention recently with the increasing popularity of intelligent user interfaces. However, compared with the endeavour in designing effective conversational search algorithms, relatively much fewer researchers have focused on the construction of benchmark datasets. For most existing datasets, the information needs are defined by researchers and search requests are not proposed by actual users. Meanwhile, these datasets usually focus on the conversations between users and agents (systems), while largely ignores the search behaviors of agents before they return response to users. To overcome these problems, we construct a Chinese Open-Domain Conversational Search Behavior Dataset (ConvSearch) based on Wizard-of-Oz paradigm in the field study scenario. We develop a novel conversational search platform to collect dialogue contents, annotate dialogue quality and candidate search results and record agent search behaviors. 25 search agents and 51 users are recruited for the field study that lasts about 45 days. The ConvSearch dataset contains 1,131 dialogues together with annotated search results and corresponding search behaviors. We also provide the intent labels of each search behavior iteration to support intent understanding related researches. The dataset is already open to public for academic usage.