Papers by Takumi Goto
Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use a Different Evaluation Process than Human? (2025.acl-short)
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| Challenge: | Existing automatic evaluation metrics are based on procedures that diverge from human evaluation. |
| Approach: | They propose to aggregate automatic evaluation metrics to bridge this gap . they propose to use edit-based metrics, -gram based metrics and sentence-level metrics to find the best ranking system. |
| Outcome: | The proposed method outperforms existing metrics on the SEEDA benchmark and improves edit-based metrics, -gram based metrics and sentence-level metrics. |
Improving Explainability of Sentence-level Metrics via Edit-level Attribution for Grammatical Error Correction (2025.acl-srw)
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| Challenge: | Existing evaluation metrics for Grammatical error correction lack explainability . lack of explainability hinders researchers from analyzing strengths and weaknesses of models . |
| Approach: | They propose to assign sentence-level scores to individual edits to improve GEC performance . they use Shapley values, from cooperative game theory, to compute contribution of each edit . |
| Outcome: | The proposed method shows that the evaluation metrics are consistent across edits and human evaluations. |
Acquiring Bidirectionality via Large and Small Language Models (2025.coling-main)
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| Challenge: | Existing unidirectional language models are still used for token-level classification tasks, but they lack bidirectionality. |
| Approach: | They propose to use bidirectional language models to train a small backward LM and concatenate its representations to those of an existing LM for downstream tasks. |
| Outcome: | The proposed model improves performance by more than 10 points in token-classification tasks and in rare domains. |
IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator (2025.findings-acl)
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| Challenge: | Existing reference-free automatic grammatical error correction methods do not correlate with human evaluation. |
| Approach: | They propose a reference-free automatic grammatical error correction evaluation method with enhanced gramma-ed capabilities. |
| Outcome: | The proposed method achieves highest correlation with human evaluations on a meta-evaluation dataset. |
Reliability Crisis of Reference-free Metrics for Grammatical Error Correction (2025.findings-emnlp)
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| Challenge: | Reference-free evaluation metrics for grammatical error correction have high correlation with human judgments, but they are not designed to evaluate adversarial systems that aim to obtain unjustifiably high scores. |
| Approach: | They propose adversarial attack strategies for four reference-free metrics . they propose SOME, Scribendi, IMPARA, and LLM-based metrics based on these metrics a . |
| Outcome: | The proposed attacks outperform the current state-of-the-art for four reference-free metrics . |
gec-metrics: A Unified Library for Grammatical Error Correction Evaluation (2025.acl-demo)
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| Challenge: | a library for using and developing grammatical error correction (GEC) evaluation metrics is released under the MIT license . |
| Approach: | They propose a library for using and developing grammatical error correction (GEC) evaluation metrics through a unified interface. |
| Outcome: | The proposed method is based on a unified evaluation framework with a strong focus on API usage and extensible. |