# Error syntax aware augmentation of feedback comment generation dataset

arXiv: https://arxiv.org/abs/2212.14293 · HTML: https://mlsanity.com/paper/2212.14293/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Interpretability for Language Learners Using Example-Based Grammatical Error Correction](https://mlsanity.com/paper/2203.07085/) (2022)
- [Classifying Syntactic Errors in Learner Language](https://mlsanity.com/paper/2010.11032/) (2020)

## Built on by (most cited first, 3 of 3)

- [Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation](https://mlsanity.com/paper/2404.15845/) (2024)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing](https://mlsanity.com/paper/2502.11368/) (2025)

## Abstract (arXiv)

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.