Shared Task on Feedback Comment Generation for Language Learners
Ryo Nagata, Masato Hagiwara, Kazuaki Hanawa, Masato Mita, Artem Chernodub, Olena Nahorna
Abstract
In this paper, we propose a generation challenge called Feedback comment generation for language learners. It is a task where given a text and a span, a system generates, for the span, an explanatory note that helps the writer (language learner) improve their writing skills. The motivations for this challenge are: (i) practically, it will be beneficial for both language learners and teachers if a computer-assisted language learning system can provide feedback comments just as human teachers do; (ii) theoretically, feedback comment generation for language learners has a mixed aspect of other generation tasks together with its unique features and it will be interesting to explore what kind of generation technique is effective against what kind of writing rule. To this end, we have created a dataset and developed baseline systems to estimate baseline performance. With these preparations, we propose a generation challenge of feedback comment generation.- Anthology ID:
- 2021.inlg-1.35
- Volume:
- Proceedings of the 14th International Conference on Natural Language Generation
- Month:
- August
- Year:
- 2021
- Address:
- Aberdeen, Scotland, UK
- Editors:
- Anya Belz, Angela Fan, Ehud Reiter, Yaji Sripada
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 320–324
- Language:
- URL:
- https://aclanthology.org/2021.inlg-1.35
- DOI:
- 10.18653/v1/2021.inlg-1.35
- Cite (ACL):
- Ryo Nagata, Masato Hagiwara, Kazuaki Hanawa, Masato Mita, Artem Chernodub, and Olena Nahorna. 2021. Shared Task on Feedback Comment Generation for Language Learners. In Proceedings of the 14th International Conference on Natural Language Generation, pages 320–324, Aberdeen, Scotland, UK. Association for Computational Linguistics.
- Cite (Informal):
- Shared Task on Feedback Comment Generation for Language Learners (Nagata et al., INLG 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.inlg-1.35.pdf