Contextual Spelling Correction with Language Model for Low-resource Setting

arXiv:2404.18072v1 Announce Type: new
Abstract: The task of Spell Correction(SC) in low-resource languages presents a significant challenge due to the availability of only a limited corpus of data and no annotated spelling correction datasets. To tackle these challenges a small-scale word-based transformer LM is trained to provide the SC model with contextual understanding. Further, the probabilistic error rules are extracted from the corpus in an unsupervised way to model the tendency of error happening(error model). Then the combination of LM …

abstract arxiv availability challenge challenges cs.cl data datasets language language model languages low scale small spell transformer type word