CIMA

Cima

Repositori ini mencakup data untuk kertas "CIMA: Konstruksi Dataset Dialog Akses Terbuka Besar untuk Bimbingan Bimbingan," yang akan disajikan di Lokakarya Aplikasi Pendidikan Bangunan (BEA) di ACL 2020.

Data tersedia di bawah lisensi Creative Commons 2.5 (https://creativecommons.org/licenses/by/2.5/).

Berikut ini adalah kunci untuk tutoraksi dan aktual, yang merupakan daftar boolean yang sesuai dengan tindakan berikut:

Kunci Tindakan Tutor: [Pertanyaan, Petunjuk/Informasi Mengungkapkan, Koreksi, Konfirmasi, Lainnya]

Kunci Tindakan Siswa: [tebak, pertanyaan, penegasan, lainnya]

Jika Anda menggunakan data ini, silakan kutip makalah kami:

 @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.",
}