Spelling Correction
50 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
Most implemented papers
An Actor-Critic Algorithm for Sequence Prediction
We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL).
MoNoise: Modeling Noise Using a Modular Normalization System
We show that MoNoise beats the state-of-the-art on different normalization benchmarks for English and Dutch, which all define the task of normalization slightly different.
Robust to Noise Models in Natural Language Processing Tasks
madrugado/robust-w2v
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ACL 2019
There are a lot of noise texts surrounding a person in modern life.
Tokenization Repair in the Presence of Spelling Errors
ad-freiburg/tokenization-repair
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CoNLL (EMNLP) 2021
We identify three key ingredients of high-quality tokenization repair, all missing from previous work: deep language models with a bidirectional component, training the models on text with spelling errors, and making use of the space information already present.
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
We extend a current sequence-tagging approach to Grammatical Error Correction (GEC) by introducing specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms.
Chinese Spelling Correction as Rephrasing Language Model
gingasan/lemon
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However, we note a critical flaw in the process of tagging one character to another, that the correction is excessively conditioned on the error.
A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages
ai-forever/sage
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Our research mainly focuses on exploring natural spelling errors and mistypings in texts and studying the ways those errors can be emulated in correct sentences to effectively enrich generative models' pre-train procedure.
Robsut Wrod Reocginiton via semi-Character Recurrent Neural Network
Inspired by the findings from the Cmabrigde Uinervtisy effect, we propose a word recognition model based on a semi-character level recurrent neural network (scRNN).
