Pith. sign in

REVIEW 1 cited by

CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.00934 v2 pith:RXVI3IA4 submitted 2024-07-01 cs.CL

classification cs.CL
keywords systemsaspectscleme2correctiondatasetserrorevaluationgrammatical
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we introduce **CLEME2.0**, a reference-based metric describing four fundamental aspects of GEC systems: hit-correction, wrong-correction, under-correction, and over-correction. They collectively contribute to exposing critical qualities and locating drawbacks of GEC systems. Evaluating systems by combining these aspects also leads to superior human consistency over other reference-based and reference-less metrics. Extensive experiments on two human judgment datasets and six reference datasets demonstrate the effectiveness and robustness of our method, achieving a new state-of-the-art result. Our codes are released at https://github.com/THUKElab/CLEME.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A new error-attribution dataset and fine-tuned judge model that outputs score, error category, and feedback for LLM responses.

Pith tools