Towards More Realistic Chinese Spell Checking with New Benchmark and Specialized Expert Model (2024.lrec-main)
Copied to clipboard
Yue Wang, Zilong Zheng, Juntao Li, Zhihui Liu, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang
| Challenge: | Large Language Models (LLMs) have been gaining attention for their ability to perform a wide range of open-domain tasks . however, the performance of LLMs has yet to be comprehensively evaluated in realistic scenarios . |
| Approach: | They propose a task to evaluate the performance of Large Language Models (LLMs) they propose RCSC task to convert Chinese text into correct text . |
| Outcome: | The proposed task evaluates the performance of existing methods in Chinese text . the realistic Chinese spell checker can achieve state-of-the-art performance on the task . |
Similar Papers
C-LLM: Learn to Check Chinese Spelling Errors Character by Character (2024.emnlp-main)
Copied to clipboard
| 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. |
Investigating Glyph-Phonetic Information for Chinese Spell Checking: What Works and What’s Next? (2023.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained Chinese language models have shown impressive performance on a wide range of NLP tasks, but the generalization ability of these models has not been well understood. |
| Approach: | They propose to use glyph-phonetic information to improve Chinese spell checking models . they propose a new, more challenging, and practical setting for testing the generalizability of CSC models. |
| Outcome: | The proposed model incorporates glyph-phonetic information and is more challenging and practical. |
ARM: An Alignment-and-Replacement Module for Chinese Spelling Check Based on LLMs (2024.emnlp-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed module improves existing models while reducing retraining and fine-tuning. |
CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers (2024.acl-long)
Copied to clipboard
| Challenge: | Existing datasets aimed at Chinese learners and native speakers are limited in size and quality. |
| Approach: | They propose a method that simulates the input process through an input method and generates large-scale pseudo data that closely resembles the actual error distribution. |
| Outcome: | The proposed method outperforms existing methods and outperformed existing models. |
A Training-free LLM-based Approach to General Chinese Character Error Correction (2025.acl-long)
Copied to clipboard
| Challenge: | Chinese spelling correction (CSC) is a crucial task that aims to correct character errors in text. |
| Approach: | They propose a task that handles missing and redundant characters and an additional prompt-based large language model to improve performance. |
| Outcome: | The proposed task is based on a high-quality dataset and a prompt-based large language model. |
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking (2021.findings-acl)
Copied to clipboard
| 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. |
| Outcome: | The proposed model outperforms strong baselines on the SIGHAN benchmarks by a large margin. |
Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters (2024.acl-long)
Copied to clipboard
Yinghui Li, Zishan Xu, Shaoshen Chen, Haojing Huang, Yangning Li, Shirong Ma, Yong Jiang, Zhongli Li, Qingyu Zhou, Hai-Tao Zheng, Ying Shen
| Challenge: | Existing studies focus on misspelled characters, ignoring faked characters which are more common and difficult to correct. |
| Approach: | They propose to use Chinese character checking to identify and correct wrong characters in texts by human annotation. |
| Outcome: | The proposed dataset is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario. |
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking (2022.findings-acl)
Copied to clipboard
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. |
Two Issues with Chinese Spelling Correction and A Refinement Solution (2024.acl-short)
Copied to clipboard
| 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. |
| Outcome: | The proposed model improves the models in all metrics by notable margins. |
PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check (2021.acl-long)
Copied to clipboard
| 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. |