Spelling Correction as a Foreign Language

Abstract

In this paper, we reformulated the spell correction problem as a machine translation task under the encoder-decoder framework. This reformulation enabled us to use a single model for solving the problem that is traditionally formulated as learning a language model and an error model. This model employs multi-layer recurrent neural networks as an encoder and a decoder. We demonstrate the effectiveness of this model using an internal dataset, where the training data is automatically obtained from user logs. The model offers competitive performance as compared to the state of the art methods but does not require any feature engineering nor hand tuning between models.

References in corpus (1)

• Sequence to Sequence Learning with Neural Networks

Cited by in corpus (4)

• GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
• Vartani Spellcheck -- Automatic Context-Sensitive Spelling Correction of OCR-generated Hindi Text Using BERT and Levenshtein Distance
• VSEC: Transformer-based Model for Vietnamese Spelling Correction
• Spelling Correction with Denoising Transformer