Spell check is a useful application which processes noisy human-generated text. Spell check for Chinese poses unresolved problems due to the large number of characters, the sparse distribution of errors, and the dearth of resources with sufficient coverage of heterogeneous and shifting error domains. For Chinese spell check, filtering using confusion sets narrows the search space and makes finding corrections easier. However, most, if not all, confusion sets used to date are fixed and thus do not include new, shifting error domains. We propose a scalable adaptable filter that exploits hierarchical character embeddings to (1) obviate the need to handcraft confusion sets, and (2) resolve sparsity problems related to infrequent errors. Our approach compares favorably with competitive baselines and obtains SOTA results on the 2014 and 2015 Chinese Spelling Check Bake-off datasets.
Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
University of Science and Technology of China（中国科学技术大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；People’s Daily Online Co.（人民网股份有限公司）；People’s Public Security University of China（中国人民公安大学）
uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers
College of Computer Science and Technology（计算机科学与技术学院）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（工业和信息化部模式分析与机器智能重点实验室）
Chinese Spelling Error Detection Using a Fusion Lattice LSTM
North China University of Technology（北方工业大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Center for Data Science, Peking University（北京大学数据科学中心）；The MOE Key Laboratory of Computational Linguistics, Peking University（教育部计算语言学重点实验室）
RAIR: Retrieval-Augmented Iterative Refinement for Chinese Spelling Correction
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongke Fanyu Technology Co., Ltd（中科泛语科技有限公司）
Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；State Key Lab of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；School of Intelligent Systems Engineering, Sun-Yat Sen University（中山大学智能工程学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
A Comprehensive Evaluation and Analysis Study for Chinese Spelling Check
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Center for Data Science, Peking University（北京大学数据科学中心）；The MOE Key Laboratory of Computational Linguistics, Peking University（北京大学计算语言学教育部重点实验室）
Bi-DCSpell: A Bi-directional Detector-Corrector Interactive Framework for Chinese Spelling Check
School of Computer Science & Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院）
Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning（湖北省人工智能与智慧学习重点实验室）；National Language Resources Monitoring and Research Center for Network Media（国家网络媒体语言资源监测与研究中心）；School of Computer, Central China Normal University（华中师范大学计算机学院）；School of Computer Science and Information Engineering, Hubei University（湖北大学计算机与信息工程学院）
A Coin Has Two Sides: A Novel Detector-Corrector Framework for Chinese Spelling Correction
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Information Management, Wuhan University（武汉大学信息管理学院）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）
Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition
Harbin Institute of Technology（哈尔滨工业大学）；iFLYTEK Research（讯飞研究院）
现有的中文文本错误检测主要集中在拼写和简单的语法错误。这些错误已被广泛研究，对人类来说相对简单。相反，汉语的语义错误研究不足，而且更加复杂，人类不容易识别。本文的任务是汉语语义错误识别（CSER），一个二元分类任务，以确定一个句子是否包含语义错误。目前的研究还没有有效的方法来解决这个问题。本文继承了BERT的模型结构，设计了多个与句法相关的预训练任务，使模型能够学习句法知识。我们的预训练任务考虑依赖结构的方向性和依赖关系的多样性。由于目前尚缺乏针对CSER的公开数据集，本文首次建立了一个高质量的CSER数据集--汉语语义可接受性语料库（Corpus of Chinese Linguistic Semantic Acceptability，CoCLSA）。在CoCLSA上的实验结果表明，我们的方法优于通用的预训练模型和语法注入模型。
CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（东华大学教育人工智能研究所）
Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters
Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Alibaba Group（阿里巴巴集团）；Baidu Inc.（百度公司）；OPPO Research Institute（OPPO研究院）；Sun-Yat Sen University（中山大学）
Fusing Bidirectional Chains of Thought and Reward Mechanisms A Method for Enhancing Question-Answering Capabilities of Large Language Models for Chinese Intangible Cultural Heritage
