A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check (D18-1)
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| Challenge: | Chinese spelling check (CSC) is a challenging but meaningful task that serves as a preprocessing in many natural language processing(NLP) applications. |
| Approach: | They propose to construct Chinese spelling check corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to OCR- and ASR-based methods. Experimental results demonstrate the effectiveness of the approach. |
| Outcome: | The proposed method is based on visual or phonologically similar spelling errors, and is validated with respect to three standard test sets. |
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| Challenge: | Chinese Spelling Correction (CSC) lacks large-scale high-quality corpora due to labor-intensive labeling of spelling errors in real-life writing or typing scenarios. |
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| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct spelling errors in sentences. |
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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 . |
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| Challenge: | Existing evaluations for Chinese spelling correction lack nuanced typology for spelling errors, creating an "invisible" bottleneck . |
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| Challenge: | Experimental results show that a sequence-to-sequence learning framework with neural networks can be effective for Chinese Spelling Correction (CSC) |
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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. |
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SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check (2020.acl-main)
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| Challenge: | Existing methods to detect and correct spelling errors in Chinese take external input or just heuristic rules. |
| Approach: | They propose to incorporate phonological and visual similarity knowledge into Chinese language models by using a specialized graph convolutional network. |
| Outcome: | The proposed method outperforms existing models on three human-annotated datasets. |
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| 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. |
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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. |