Spelling Correction
21 papers with code • 0 benchmarks • 2 datasets
Spelling correction is the task of detecting and correcting spelling mistakes.
Benchmarks
GitHub Typo Corpus
Viwiki-Spelling
Greatest papers with code
An Actor-Critic Algorithm for Sequence Prediction
joeynmt/joeynmt
•
•
We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL).
Ranked #7 on
Machine Translation
on IWSLT2015 English-German
GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
The lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GEC).
NeuSpell: A Neural Spelling Correction Toolkit
neuspell/neuspell
•
•
EMNLP 2020
We introduce NeuSpell, an open-source toolkit for spelling correction in English.
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings
We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings.
DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding
Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks.
Kyoto University Participation to WAT 2016
We report very good translation results, especially when using neural MT for Chinese-to-Japanese translation.
Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
FDChongli/TwoWaysToImproveCSC
•
•
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.
Context-aware Stand-alone Neural Spelling Correction
jacklxc/StandAloneSpellingCorrection
•
•
Findings of the Association for Computational Linguistics 2020
Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings.
Systematically Adapting Machine Translation for Grammatical Error Correction
Our model rivals the current state of the art using a fraction of the training data.