Error syntax aware augmentation of feedback comment generation dataset

TL;DR

This paper introduces a syntax-aware data augmentation method for feedback comment generation in language learning, improving model performance by leveraging syntactical dependencies during fine-tuning of T5, and achieved second place in the shared task.

Contribution

The paper proposes a novel syntax-aware augmentation technique for feedback comment generation, enhancing the effectiveness of T5-based models in educational NLP tasks.

Findings

Achieved second place in GenChal 2022 shared task.

Enhanced feedback comment generation through syntax-aware data augmentation.

Demonstrated the effectiveness of syntactical dependency-based augmentation.

Abstract

This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a span of the error, a system generates an explanatory note that helps the writer (language learner) to improve their writing skills. Our solution is based on fine-tuning the T5 model on the initial dataset augmented according to syntactical dependencies of the words located within indicated error span. The solution of our team "nigula" obtained second place according to manual evaluation by the organizers.