  Building an independent misspelling detector and serve it before correction
can bring multiple benefits to speller and other search components, which is
particularly true for the most commonly deployed noisy-channel based speller
systems. With rapid development of deep learning and substantial advancement in
contextual representation learning such as BERTology, building a decent
misspelling detector without having to rely on hand-crafted features associated
with noisy-channel architecture becomes more-than-ever accessible. However
BERTolgy models are trained with natural language corpus but Maps Search is
highly domain specific, would BERTology continue its success. In this paper we
design 4 stages of models for misspeling detection ranging from the most basic
LSTM to single-domain augmented fine-tuned BERT. We found for Maps Search in
our case, other advanced BERTology family model such as RoBERTa does not
necessarily outperform BERT, and a classic cross-domain fine-tuned full BERT
even underperforms a smaller single-domain fine-tuned BERT. We share more
findings through comprehensive modeling experiments and analysis, we also
briefly cover the data generation algorithm breakthrough.
