Spelling correction is the task of detecting and correcting spelling mistakes.
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
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).
We introduce NeuSpell, an open-source toolkit for spelling correction in English.
We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings.
Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks.
We report very good translation results, especially when using neural MT for Chinese-to-Japanese translation.
Our model rivals the current state of the art using a fraction of the training data.
GRAMMATICAL ERROR CORRECTION MACHINE TRANSLATION SPELLING CORRECTION
Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings.