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Typographical errors are a major source of frustration for visitors of onlinemarketplaces. Because of the domain-specific nature of these marketplaces andthe very short queries users tend to search for, traditional spell chekingsolutions do not perform well in correcting typos. We present a dataaugmentation method to address the lack of annotated typo data and train arecurrent neural network to learn context-limited domain-specific embeddings.Those embeddings are deployed in a real-time inferencing API for the MicrosoftAppSource marketplace to find the closest match between a misspelled user queryand the available product names. Our data efficient solution shows thatcontrolled high quality synthetic data may be a powerful tool especiallyconsidering the current climate of large language models which rely onprohibitively huge and often uncontrolled datasets.

Domain specificity and data efficiency in typo tolerant spell checkers: the case of search in online marketplaces