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 applications in human language
understanding. The phonetically or visually similar yet semantically distinct
characters make it an arduous task in any language. Earlier efforts on spelling
error correction in Bangla and resource-scarce Indic languages focused on
rule-based, statistical, and machine learning-based methods which we found
rather inefficient. In particular, machine learning-based approaches, which
exhibit superior performance to rule-based and statistical methods, are
ineffective as they correct each character regardless of its appropriateness.
In this work, we propose a novel detector-purificator-corrector framework based
on denoising transformers by addressing previous issues. Moreover, we present a
method for large-scale corpus creation from scratch which in turn resolves the
resource limitation problem of any left-to-right scripted language. The
empirical outcomes demonstrate the effectiveness of our approach that
outperforms previous state-of-the-art methods by a significant margin for
Bangla spelling error correction. The models and corpus are publicly available
at this https URL.

本文介绍如何利用去噪变形器来解决自然语言处理中的拼写错误纠正问题，还提出了一种从头创建大规模语料库的方法，并在孟加拉语的拼写错误纠正中获得显著性能优于现有方法的结果。