IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator
This work addresses the need for better automatic evaluation in grammatical error correction, offering a domain-specific improvement for researchers and practitioners in natural language processing.
The paper tackles the problem of evaluating grammatical error correction (GEC) by proposing IMPARA-GED, a reference-free method with grammatical error detection (GED) capabilities, which achieves the highest correlation with human sentence-level evaluations on the SEEDA dataset.
We propose IMPARA-GED, a novel reference-free automatic grammatical error correction (GEC) evaluation method with grammatical error detection (GED) capabilities. We focus on the quality estimator of IMPARA, an existing automatic GEC evaluation method, and construct that of IMPARA-GED using a pre-trained language model with enhanced GED capabilities. Experimental results on SEEDA, a meta-evaluation dataset for automatic GEC evaluation methods, demonstrate that IMPARA-GED achieves the highest correlation with human sentence-level evaluations.