WSpeller: Robust Word Segmentation for Enhancing Chinese Spelling Check (2022.findings-emnlp)
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| Challenge: | Chinese spelling check (CSC) detects and corrects spelling errors in Chinese texts. |
| Approach: | They propose a Chinese spelling check model that takes into account word segmentation and a module that can assist the correction module by predicting correct word segmentations from sentences containing spelling errors. |
| Outcome: | The proposed model outperforms baselines on SIGHAN13, SIGHEN14, and SIGHAN15 and maintains equal performance on SSGHAN14. |
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| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct spelling errors in sentences. |
| Approach: | They propose a Chinese Spell Checking method that learns to check errors Character by Character. |
| Outcome: | The proposed method achieves a 2.1% enhancement in general scenarios and a significant improvement in vertical domain scenarios compared to existing methods. |
Two Issues with Chinese Spelling Correction and A Refinement Solution (2024.acl-short)
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| Challenge: | Existing models contain many spelling errors, resulting in performance bottlenecks and performance issues. |
| Approach: | They propose to fix the SIGHAN datasets and re-evaluate four representative Chinese Spelling Correction models using the fixed datasets. |
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ARM: An Alignment-and-Replacement Module for Chinese Spelling Check Based on LLMs (2024.emnlp-main)
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| Challenge: | Chinese Spelling Check (CSC) aims to identify and correct spelling errors in Chinese texts, where enhanced semantic understanding of a sentence can significantly improve correction accuracy. |
| Approach: | They propose a plug-and-play Alignment-and -Replacement module that enhances existing Chinese CSC models without retraining or fine-tuning. |
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PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check (2021.acl-long)
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| Challenge: | False gram and phonological errors make Chinese spelling check difficult . a novel end-to-end trainable model outperforms existing methods . |
| Approach: | They propose a trainable Chinese spelling check model that integrates phonological and visual information into a pre-trained language model. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on three benchmarks. |
Dynamic Connected Networks for Chinese Spelling Check (2021.findings-acl)
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| Challenge: | Chinese spelling check (CSC) is a task to detect and correct spelling errors in Chinese text. |
| Approach: | They propose a new architecture which generates Chinese characters via a Pinyin Enhanced Candidate Generator and then utilizes an attention-based network to model the dependencies between two adjacent Chinese characters. |
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The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking (2022.findings-acl)
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Yinghui Li, Qingyu Zhou, Yangning Li, Zhongli Li, Ruiyang Liu, Rongyi Sun, Zizhen Wang, Chao Li, Yunbo Cao, Hai-Tao Zheng
| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct spelling errors, which are caused by the phonological or visual similarity. |
| Approach: | They propose an Error-driven COntrastive Probability Optimization framework to refine the knowledge representations of pre-trained language models to avoid predicting common characters. |
| Outcome: | Extensive experiments and detailed analyses on SIGHAN datasets demonstrate that ECOPO is simple yet effective. |
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking (2021.findings-acl)
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| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for usergenerated text in Chinese. |
| Approach: | They propose a Chinese spell checker that leverages multimodal Chinese characters' information to predict the correct output. |
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Driving Chinese Spelling Correction from a Fine-Grained Perspective (2025.coling-main)
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| Challenge: | Existing evaluations for Chinese spelling correction lack nuanced typology for spelling errors, creating an "invisible" bottleneck . |
| Approach: | They propose a fine-grained evaluation principle for Chinese spelling correction (CSC) they categorize spelling errors into six different types and use it to evaluate models . |
| Outcome: | The proposed evaluation principle can be leveraged to enhance CSC training models. |
Bi-DCSpell: A Bi-directional Detector-Corrector Interactive Framework for Chinese Spelling Check (2024.findings-emnlp)
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| Challenge: | Chinese Spelling Check (CSC) aims to detect and correct potentially misspelled characters in Chinese sentences. |
| Approach: | They propose a bi-directional Detector-Corrector framework for Chinese Spelling Check which mutually enhances the feature representation for detection and correction subtasks. |
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SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check (2021.emnlp-main)
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| Challenge: | Chinese Spelling Check is a nontrivial task because of the nature of ideographic language. |
| Approach: | They propose a pretrained model with graph-based extra features that captures erroneous patterns . they use a graph neural network to introduce radical and pinyin information as visual and phonetic features. |
| Outcome: | The proposed model can show competitive performance on OCR datasets where most errors are not covered by existing confusion set. |