Spelling Correction
42 papers with code • 0 benchmarks • 4 datasets
Spelling correction is the task of detecting and correcting spelling mistakes.
Benchmarks
These leaderboards are used to track progress in Spelling Correction
GitHub Typo Corpus
CSCD-IME
MCSCSet
Viwiki-Spelling
Latest papers
CSCD-IME: Correcting Spelling Errors Generated by Pinyin IME
In fact, most of Chinese input is based on pinyin input method, so the study of spelling errors in this process is more practical and valuable.
MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction
In this work, we define the task of Medical-domain Chinese Spelling Correction and propose MCSCSet, a large scale specialist-annotated dataset that contains about 200k samples.
Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino
With 84. 75 million Filipinos online, the ability for models to process online text is crucial for developing Filipino NLP applications.
BSpell: A CNN-Blended BERT Based Bangla Spell Checker
hasiburshanto/bangla-spell-checker
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A specialized BERT model named BSpell has been proposed in this paper targeted towards word for word correction in sentence level.
ABB-BERT: A BERT model for disambiguating abbreviations and contractions
Abbreviations and contractions are commonly found in text across different domains.
Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems
In this work, we introduce a novel approach to do contextual biasing by adding a contextual spelling correction model on top of the end-to-end ASR system.
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning
Machine learning-based program analysis methods use variable name representations for a wide range of tasks, such as suggesting new variable names and bug detection.
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction
liushulinle/plome
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ACL 2021
In this paper, we propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) for CSC, which jointly learns how to understand language and correct spelling errors.
BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks
yannikkellerde/BERT-Defense
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Findings (ACL) 2021
Adversarial attacks expose important blind spots of deep learning systems.
Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
FDChongli/TwoWaysToImproveCSC
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ACL 2021
A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one.
