DOI: 10.5061/dryad.q573n5ttw
title: Decopy: Detect and correct with Pinyin for Chinese spelling correction

description: Chinese Spelling Correction (CSC) is a crucial task. Previous studies have
 been affected by issues such as misleading error information,
 over-reliance on high-frequency characters, and scarcity of training data.
 This paper proposes a new CSC model, named Decopy, which employs an
 advanced detection-correction framework and a novel error masking strategy
 with pinyin features. Decopy can not only capture semantic information
 (word embeddings) and positional information (position embeddings) of
 words, but also recognize the phonetic features (pinyin embeddings) of
 words. It can start directly from the phonetics of words, connect
 similarities at the pinyin level, and make the most of useful phonetic
 information, thereby reducing reliance on confusions and minimizing
 misleading information. Additionally, to address the scarcity of training
 data, we have constructed a new CSC dataset based on THUCNews and used it
 for pre-training Decopy. This enables Decopy to have a more comprehensive
 understanding of the input information, especially the additional pinyin
 information. Experiments on SIGHAN15 and three domain-specific datasets,
 namely LAW, Medical (Med), and Official Document Writing (Odw), show that
 Decopy achieves significant improvements and outperforms the previous
 state-of-the-art methods. Finally, we tested and analyzed several
 high-performance LLMs on the CSC task, and fine-tuned ChatGLM3-6B for the
 CSC task to further evaluate the capabilities of LLMs in this field.
description: # Chinese Spelling Correction Dataset: [Open Source
 Data](https://modelscope.cn/datasets/heaodong/SIGHAN2015/files) --- First
 of all, it is important to clarify that, given our paper centers on the
 research of Chinese spelling correction, it is reasonable to anticipate
 that all the datasets we employ will necessarily include Chinese content.
 We have submitted our training dataset (**train.json**), validation
 dataset (**dev.json**), test dataset (**test.json**). The whole dataset
 includes 10000 manually annotated samples from SIGHAN15 and 271000
 automatically generated samples provided by Wang et al (paper:
 Confusionset-guided pointer networks for Chinese spelling check). Our
 model is fine-tuned using this dataset with a batch size of 32 and a
 learning rate of 2e-5. ## Descriptions ### **train.json** * *id*: A unique
 identifier for the data entry. * *original_text*: The original text, which
 may contain errors. * *wrong_ids*: An array indicating the indices of
 incorrect characters in original_text, with indexing starting from 0. *
 *correct_text*: The corrected version of the text. ### **dev.json** *
 *id*: A unique identifier for the data entry. * *original_text*: The
 original text, which may contain errors. * *wrong_ids*: An array
 indicating the indices of incorrect characters in original_text, with
 indexing starting from 0. * *correct_text*: The corrected version of the
 text. ### **test.json** * *id*: A unique identifier for the data entry. *
 *original_text*: The original text, which may contain errors. *
 *wrong_ids*: An array indicating the indices of incorrect characters in
 original_text, with indexing starting from 0. * *correct_text*: The
 corrected version of the text. ## Key Information Sources The
 domain-specific dataset ECSpell can be accessed at GitHub repository:
 [https://github.com/aopolin-lv/ECSpell](https://github.com/aopolin-lv/ECSpell) ## Code/Software The code/software and operation instructions used in this study are publicly available on the GitHub platform as well as Zenodo: [https://github.com/atri45/Decopy](https://github.com/atri45/Decopy)