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）
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.
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）
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）
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）
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））
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）
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）
Context-sensitive Spelling Correction Using Google Web 1T 5-Gram Information
Lebanese Association for Computational Sciences (LACSC)（黎巴嫩计算科学协会）
在计算机中，拼写检查是检测文本中拼写错误的单词并有时提供拼写建议的过程。基本上，拼写检查器是一种使用单词字典来执行拼写检查的计算机程序。字典越大，错误检测率越高。拼写检查器基于常规词典的事实，它们遭受数据稀疏问题，因为它们不能捕获大量词汇，包括专有名称，特定领域术语，技术术语，特殊首字母缩略词和术语。因此，它们表现出较低的错误检测率，并且经常无法捕获文本中的主要错误。本文提出了一种新的上下文相关的拼写校正方法，用于检测和纠正数字文本文档中的非单词和真实单词错误。该方法基于Google Web 1 T 5-gram数据集的数据统计，该数据集由从万维网中提取的大量n-gram单词序列组成。基本上，所提出的方法包括检测拼写错误的错误检测器、基于字符2-gram模型生成校正建议的候选拼写生成器以及执行上下文错误校正的错误校正器。在一组来自不同领域的文本文档上进行的实验表明，该方法具有出色的拼写错误纠正率，并大大减少了非单词和真实单词错误。在进一步的研究中，所提出的算法是并行化，以降低错误检测和纠正过程的计算成本。
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）
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care
Meetyou AI Lab（美柚AI实验室）；Xiamen Key Laboratory of Women’s Internet Health Management（厦门市女性互联网健康管理重点实验室）；Southwest University of Finance and Economics（西南财经大学）；Osaka University（大阪大学）
Evaluating Reliability Asymmetries in Chinese Factual Search and AI Answers
Department of Electronics, Information and Bioengineering, Politecnico di Milano（电子、信息与生物工程系，米兰理工学院）；University of Science and Technology of China（中国科学技术大学）
