CIMA

西马

该存储库包括论文“ CIMA：用于辅导的大型开放访问对话数据集”的数据，该数据将在ACL 2020的建筑教育应用程序（BEA）研讨会上介绍。

数据可根据创意共享2.5许可（https://creativecommons.org/licenses/by/2.5/）提供。

以下是辅导和学生行为的关键，这是与以下操作相对应的布尔人的列表：

导师行动键：[问题，提示/信息揭示，更正，确认等]

学生行动键：[猜测，问题，肯定，其他]

如果您使用这些数据，请引用我们的论文：

 @inproceedings{stasaski-etal-2020-cima,
    title = "{CIMA}: A Large Open Access Dialogue Dataset for Tutoring",
    author = "Stasaski, Katherine  and
      Kao, Kimberly  and
      Hearst, Marti A.",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.5",
    pages = "52--64",
    abstract = "One-to-one tutoring is often an effective means to help students learn, and recent experiments with neural conversation systems are promising. However, large open datasets of tutoring conversations are lacking. To remedy this, we propose a novel asynchronous method for collecting tutoring dialogue via crowdworkers that is both amenable to the needs of deep learning algorithms and reflective of pedagogical concerns. In this approach, extended conversations are obtained between crowdworkers role-playing as both students and tutors. The CIMA collection, which we make publicly available, is novel in that students are exposed to overlapping grounded concepts between exercises and multiple relevant tutoring responses are collected for the same input. CIMA contains several compelling properties from an educational perspective: student role-players complete exercises in fewer turns during the course of the conversation and tutor players adopt strategies that conform with some educational conversational norms, such as providing hints versus asking questions in appropriate contexts. The dataset enables a model to be trained to generate the next tutoring utterance in a conversation, conditioned on a provided action strategy.",
}