Refining Czech GEC: Insights from a Multi-Experiment Approach

• URL: http://arxiv.org/abs/2506.22402v1
• Date: Fri, 27 Jun 2025 17:21:40 GMT
• Title: Refining Czech GEC: Insights from a Multi-Experiment Approach
• Authors: Petr Pechman, Milan Straka, Jana Straková, Jakub Náplava,
• Abstract summary: We present a grammar error correction (GEC) system that achieves state of the art for the Czech language.<n>Our system is based on a neural network translation approach with the Transformer architecture.<n>Key feature is its real-time synthetic generation pipeline, which dynamically augments sentences with artificial errors.
• Score: 2.4792831876409718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a grammar error correction (GEC) system that achieves state of the art for the Czech language. Our system is based on a neural network translation approach with the Transformer architecture, and its key feature is its real-time synthetic generation pipeline, which dynamically augments sentences with artificial errors by introducing both language-agnostic and Czech-specific errors. We conduct a comprehensive series of experiments, investigating the Czech GEC corpora as bases for synthetic error introduction, several error generation strategies, domain balancing, tokenization granularity, model size, and data scaling during fine-tuning. Additionally, we evaluate the performance of large language models (LLMs) on Czech GEC in both end-user and expert fine-tuning scenarios. Our best-performing model is superior both in performance and computational efficiency. The source code and the trained model links are available on https://github.com/ufal/tsd2025-gec.

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