IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 3 Jun 2025.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
- 2024Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inferencecited by 554
- 2020Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correctioncited by 59
- 2022Revisiting Grammatical Error Correction Evaluation and Beyondcited by 18
- 2018Automatic Metric Validation for Grammatical Error Correctioncited by 17
- 2018Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplificationcited by 15
- 2025gec-metrics: A Unified Library for Grammatical Error Correction Evaluationcited by 9
- 2025Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use a Different Evaluation Process than Human?cited by 7
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Citing papers per year: 2025: 2.
2 papers.