Abstract
In this paper, we present strong baselines for the task of Feedback Comment Generation for Writing Learning. Given a sentence and an error span, the task is to generate a feedback comment explaining the error. Sentences and feedback comments are both in English. We experiment with LLMs and also create multiple pseudo datasets for the task, investigating how it affects the performance of our system. We present our results for the task along with extensive analysis of the generated comments with the aim of aiding future studies in feedback comment generation for English language learners.- Anthology ID:
- 2023.inlg-genchal.8
- 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:
- 53–59
- Language:
- URL:
- https://aclanthology.org/2023.inlg-genchal.8
- DOI:
- Cite (ACL):
- Shabnam Behzad, Amir Zeldes, and Nathan Schneider. 2023. Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges, pages 53–59, Prague, Czechia. Association for Computational Linguistics.
- Cite (Informal):
- Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help? (Behzad et al., INLG-SIGDIAL 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.inlg-genchal.8.pdf