Neural Machine Translation

Deep models for grammar correction

Modern grammar correction methods are mainly transformer-based neural machine translation (NMT) models. For spelling correction, transformers can be studied as a machine translation problem, where instead of translating from a source language to a target language, we can use misspelled words as the "source language”, and correctly spelled words in our language of choice as the "target language". For grammar correction, we can modify this architecture, instead translating a sentence from a grammatically incorrect sentence as our "source language" and a grammatically correct sentence as our "target language". Since these problems are quite similar, many modern methods combine these two to create a single transformer that can handle both spelling and grammar correction.