Spelling Correction
Spelling correction is the task of detecting and correcting spelling mistakes.
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Greatest papers with code
An Actor-Critic Algorithm for Sequence Prediction
• joeynmt/joeynmt
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We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL).
#7 best model for
Machine Translation
on IWSLT2015 English-German
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.
Kyoto University Participation to WAT 2016
We report very good translation results, especially when using neural MT for Chinese-to-Japanese translation.
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.
Systematically Adapting Machine Translation for Grammatical Error Correction
Our model rivals the current state of the art using a fraction of the training data.
GRAMMATICAL ERROR CORRECTION MACHINE TRANSLATION SPELLING CORRECTION
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).
Neural Chinese Word Segmentation as Sequence to Sequence Translation
In this paper, we cast the CWS as a sequence translation problem and propose a novel sequence-to-sequence CWS model with an attention-based encoder-decoder framework.
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.
SOTA for Lexical Normalization on LexNorm