NTOU Chinese Spelling Check System in Sighan-8 Bake-off

Chu, Wei-Cheng; Lin, Chuan-Jie

doi:10.18653/v1/w15-3121

Cited by 15 publications

(14 citation statements)

References 17 publications

(13 reference statements)

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“…The early days of CSC focused on error detection and correction by means of rules, the design of which required linguistic knowledge and feature engineering [8]. Lack of migration and generalization capabilities.…”

Section: Related Work 21 Chinese Spell Checkingmentioning

confidence: 99%

Chinese spelling error correction by multi-task learning with pronunciation gap predictor

Pan

2023

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

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Chinese Spell Check (CSC) aims to detect and correct spelling errors in Chinese text, almost all of which are related to phonetic or visual similarity. Large-scale pre-trained models (PLMs) are currently making substantial progress on the CSC task. However, when correcting errors, PLMs tend to select those words that are semantically sound or expressively fluent, sometimes ignoring pronunciation similarities. Meanwhile, the models lack knowledge of pronunciation differences. To address this problem, we propose a multi-task learning model to help enhance the CSC task. The auxiliary task is to estimate the degree of pronunciation gap between the original input and the corresponding correct text from the granularity of each word. Specifically, we use the edit distance of Pinyin to measure the degree of pronunciation discrepancy. The edit distance scheme we use is modified, due to the specificity of the Pinyin structure. Experiments on a open available benchmark dataset demonstrate the effectiveness of our strategy.

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Section: Related Work 21 Chinese Spell Checkingmentioning

confidence: 99%

Chinese spelling error correction by multi-task learning with pronunciation gap predictor

Pan

2023

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

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“…We compare REALISE with the following baselines: KUAS , NTOU (Chu and Lin, 2015), NCTU-NTUT (Wang and Liao, 2015), HanSpeller++ , LMC (Xie et al, 2015) mainly utilize heuristics or traditional machine learning algorithms, such as n-gram language model, Conditional Random Field and Hidden Markov Model. Sequence Labeling (Wang et al, 2018) treats CSC as a sequence labeling problem and applies a BiLSTM model.…”

Section: Baselinesmentioning

confidence: 99%

“…The CSC task is to detect and correct spelling errors in Chinese sentences. Early works design various rules to deal with different errors Chu and Lin, 2015). Next, traditional machine learning algorithms are brought to this field, such as Conditional Random Field and Hidden Markov Model (Wang and Liao, 2015;.…”

Section: Chinese Spell Checkingmentioning

confidence: 99%

Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking

Xu¹,

Li²,

Zhou³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for usergenerated text in Chinese language. Most of the Chinese spelling errors are misused semantically, phonetically or graphically similar characters. Previous attempts notice this phenomenon and try to utilize the similarity relationship for this task. However, these methods use either heuristics or handcrafted confusion sets to predict the correct character. In this paper, we propose a Chinese spell checker called REALISE, by directly leveraging the multimodal information of the Chinese characters. The REALISE model tackles the CSC task by (1) capturing the semantic, phonetic and graphic information of the input characters, and (2) selectively mixing the information in these modalities to predict the correct output. Experiments 1 on the SIGHAN benchmarks show that the proposed model outperforms strong baselines by a large margin.

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“…Transfer Learning using BERT Spell check requires analyzing the syntactic plausibility of sentences. In Chinese, a common spelling mistake is confusing the pairs 的 and 地 (Chu and Lin, 2015). Both has the same pronunciation ("de").…”

Section: Improving Chinese Spell Check Bymentioning

confidence: 99%

Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check

Nguyen¹,

Ngo²,

Chen³

2020

Preprint

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Spell check is a useful application which involves processing noisy human-generated text. Compared to other languages like English, it is more challenging to detect and correct spelling errors in Chinese because it has more (up to 100k) characters. For Chinese spell check, 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, evolving error patterns. We propose a scalable approach to adapt confusion sets by exploiting hierarchical character embeddings to (1) obviate the need to handcraft confusion sets, and (2) resolve sparsity issues related to seldomoccurring errors. Our approach establishes new SOTA results in spelling error correction on the 2014 and 2015 Chinese Spelling Correction Bake-off datasets.

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NTOU Chinese Spelling Check System in Sighan-8 Bake-off

Cited by 15 publications

References 17 publications

Chinese spelling error correction by multi-task learning with pronunciation gap predictor

Chinese spelling error correction by multi-task learning with pronunciation gap predictor

Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking

Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check

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