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September 10, 2025Open Access

Transformer-Based Grammatical Error Correction Using Sequence Modeling

Key Points

Using a transformer-based approach significantly enhances grammatical error correction outcomes.
The model, evaluated with the BLEU score, demonstrates robust performance in correcting grammatical mistakes.
A custom parallel dataset of erroneous and corrected sentences facilitated effective model fine-tuning.
This study indicates that even small synthetic datasets can achieve meaningful benefits in natural language processing.

Abstract

Grammatical Error Correction (GEC) is a core natural language processing task with significant applications in educational technology and writing support tools. This paper presents a transformer-based approach using the T5 model for correcting grammar errors. We construct a custom parallel dataset of erroneous and corrected sentences, fine-tune the model using the Hugging Face Transformers library, and evaluate performance using BLEU score. Our work demonstrates how even modest, synthetic datasets can produce meaningful improvements through neural sequence modeling.

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Cite This Study

A. S. Shaw (Mon,) studied this question.

synapsesocial.com/papers/68c1b18554b1d3bfb60e84bc https://doi.org/https://doi.org/10.36227/techrxiv.175433178.81106099/v1

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