We introduce the StatCan Dialogue Dataset consisting of 19,379 conversation turns between agents working at Statistics Canada and online users looking for published data tables. The conversations stem from genuine intents, are held in English or French, and lead to agents retrieving one of over 5000 complex data tables. Based on this dataset, we propose two tasks: (1) automatic retrieval of relevant tables based on a on-going conversation, and (2) automatic generation of appropriate agent responses at each turn. We investigate the difficulty of each task by establishing strong baselines. Our experiments on a temporal data split reveal that all models struggle to generalize to future conversations, as we observe a significant drop in performance across both tasks when we move from the validation to the test set. In addition, we find that response generation models struggle to decide when to return a table. Considering that the tasks pose significant challenges to existing models, we encourage the community to develop models for our task, which can be directly used to help knowledge workers find relevant tables for live chat users.

该研究介绍了StatCan Dialogue Dataset数据集，并提出了基于该数据集的两个任务：（1）基于正在进行中的对话自动检索相关表格，（2）自动生成适当的代理响应。该研究发现基于该数据集的模型对未来的对话泛化能力不足，并且响应生成模型难以判断何时返回表格。由于任务对现有模型存在显著挑战，作者鼓励学术界开发特定于此任务的模型，以帮助知识工作者为在线用户找到相关图表。

StatCan对话数据集：通过具有真实意图的对话检索数据表