A reference-free GEC evaluation method that combines grammatical error detection pre-training with IMPARA's quality estimator achieves the highest sentence-level correlation with human judgments on SEEDA-S.
gec-metrics: A Unified Library for Grammatical Error Correction Evaluation
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abstract
We introduce gec-metrics, a library for using and developing grammatical error correction (GEC) evaluation metrics through a unified interface. Our library enables fair system comparisons by ensuring that everyone conducts evaluations using a consistent implementation. Moreover, it is designed with a strong focus on API usage, making it highly extensible. It also includes meta-evaluation functionalities and provides analysis and visualization scripts, contributing to developing GEC evaluation metrics. Our code is released under the MIT license and is also distributed as an installable package. The video is available on YouTube.
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IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator
A reference-free GEC evaluation method that combines grammatical error detection pre-training with IMPARA's quality estimator achieves the highest sentence-level correlation with human judgments on SEEDA-S.