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 …

Spelling error correction is the task of identifying and rectifying misspelled words in texts. It is
a potential and active research topic in Natural Language Processing because of numerous …

Spelling correction is considered a challenging task for resource-scarce languages. The
Arabic language is one of these resource-scarce languages, which suffers from the absence …

Traditional statistical approaches to spelling correction usually consist of two consecutive
processes–error detection and correction–and they are generally computationally intensive …

Writing, as an omnipresent form of human communication, permeates nearly every aspect of
contemporary life. Consequently, inaccuracies or errors in written communication can lead …

Existing BERT-based models for Chinese spelling correction (CSC) have three issues. 1)
Bert tends to rectify a correct low-frequency collocation into a high-frequency one and leads …

Although existing neural network approaches have achieved great success on Chinese
spelling correction, there is still room to improve. The model is required to avoid over …

We propose a nested recurrent neural network (nested RNN) model for English spelling
error correction and generate pseudo data based on phonetic similarity to train it. The model …

Natural language processing (NLP) is a fascinating area of artificial intelligence. It allows
humans to interact with machines through natural language. There are two main concepts in …