The applications of conversational agents for scientific disciplines (as expert domains) are understudied due to the lack of dialogue data to train such agents. While most data collection frameworks, such as Amazon Mechanical Turk, foster data collection for generic domains by connecting crowd workers and task designers, these frameworks are not much optimized for data collection in expert domains. Scientists are rarely present in these frameworks due to their limited time budget. Therefore, we introduce a novel framework to collect dialogues between scientists as domain experts on scientific papers. Our framework lets scientists present their scientific papers as groundings for dialogues and participate in dialogue they like its paper title. We use our framework to collect a novel argumentative dialogue dataset, ArgSciChat. It consists of 498 messages collected from 41 dialogues on 20 scientific papers. Alongside extensive analysis on ArgSciChat, we evaluate a recent conversational agent on our dataset. Experimental results show that this agent poorly performs on ArgSciChat, motivating further research on argumentative scientific agents. We release our framework and the dataset.

本研究为缺少对话数据来训练这种代理而使科学学科（作为专家领域）的对话代理应用研究不足，提出了一种新的框架，用于在科学论文上收集科学家之间的对话，并通过ArgSciChat数据集进行了 extensive analysis，在此数据集上评估了最新的对话代理并发现其表现较差，促进了关于争议性科学代理的进一步研究。

ArgSciChat：一份关于科学论文争辩对话的数据集