Abstract
In the context of language learning, feedback comment generation is the task of generating hints or explanatory notes for learner texts that help understand why a part of text is erroneous. This paper presents our approach to the Feedback Comment Generation Shared Task, collocated with the 16th International Natural Language Generation Conference (INLG 2023). The approach augments the generation of feedback comments by a self-supervised identification of feedback types in a multitasklearning setting. Within the shared task, other approaches performed more effective, yet the combined modeling of feedback type classification and feedback comment generation is superior to performing eedback generation only.- Anthology ID:
- 2023.inlg-genchal.5
- Volume:
- Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges
- Month:
- September
- Year:
- 2023
- Address:
- Prague, Czechia
- Editor:
- Simon Mille
- Venues:
- INLG | SIGDIAL
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 31–36
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.inlg-genchal.5/
- DOI:
- Cite (ACL):
- Maja Stahl and Henning Wachsmuth. 2023. Identifying Feedback Types to Augment Feedback Comment Generation. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges, pages 31–36, Prague, Czechia. Association for Computational Linguistics.
- Cite (Informal):
- Identifying Feedback Types to Augment Feedback Comment Generation (Stahl & Wachsmuth, INLG-SIGDIAL 2023)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.inlg-genchal.5.pdf