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

TL;DR

This paper investigates the effectiveness of data augmentation using pseudo datasets in improving feedback comment generation for English language learners, utilizing large language models and providing extensive analysis.

Contribution

It introduces strong baselines for feedback comment generation and explores the impact of data augmentation with pseudo datasets on system performance.

Findings

Data augmentation improves feedback comment quality.

Large language models outperform traditional methods.

Extensive analysis guides future research in feedback generation.

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.