Error syntax aware augmentation of feedback comment generation dataset
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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.
Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 4 of its 17 references that are in the map. First posted on arXiv 29 Dec 2022.
Builds on
4 of 17 references.
References · 4
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,549
- 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,379
- 2022Interpretability for Language Learners Using Example-Based Grammatical Error Correctioncited by 13
- 2020Classifying Syntactic Errors in Learner Languagecited by 11
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.
- 2024Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generationcited by 63
- 2023On the application of Large Language Models for language teaching and assessment technologycited by 27
- 2025LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writingcited by 10
- 2025LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing17 Feb 2025
- 2024Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation24 Apr 2024
- 2023On the application of Large Language Models for language teaching and assessment technology17 Jul 2023