mlsanityData to 7 Oct 2026

A Comprehensive Evaluation and Analysis Study for Chinese Spelling Check

arXiv 2307.13655 ↗First posted 25 Jul 2023cs.CLcited by 3

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Abstract (arXiv)

With the development of pre-trained models and the incorporation of phonetic and graphic information, neural models have achieved high scores in Chinese Spelling Check (CSC). However, it does not provide a comprehensive reflection of the models' capability due to the limited test sets. In this study, we abstract the representative model paradigm, implement it with nine structures and experiment them on comprehensive test sets we constructed with different purposes. We perform a detailed analysis of the results and find that: 1) Fusing phonetic and graphic information reasonably is effective for CSC. 2) Models are sensitive to the error distribution of the test set, which reflects the shortcomings of models and reveals the direction we should work on. 3) Whether or not the errors and contexts have been seen has a significant impact on models. 4) The commonly used benchmark, SIGHAN, can not reliably evaluate models' performance.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 25 Jul 2023.

Builds on

15 of 33 references.

References · 15

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3 more
  1. 2020Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Checkcited by 8
  2. 2021Do Grammatical Error Correction Models Realize Grammatical Generalization?cited by 6
  3. 2021Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errorscited by 2

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024C-LLM: Learn to Check Chinese Spelling Errors Character by Charactercited by 12
  2. 2024Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learningcited by 3
  3. 2025CEC-Zero: Chinese Error Correction Solution Based on LLMcited by 0