Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?

Shabnam Behzad, Amir Zeldes, Nathan Schneider


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:
Bibkey:
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)
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PDF:
https://aclanthology.org/2023.inlg-genchal.8.pdf