Domain specificity and data efficiency in typo tolerant spell checkers:
  the case of search in online marketplaces
Dayananda Ubrangala Juhi Sharma Ravi Prasad Kondapalli Kiran R Amit Agarwala Laurent Boué
Typographical errors are a major source of frustration for visitors of online
marketplaces. Because of the domain-specific nature of these marketplaces and
the very short queries users tend to search for, traditional spell cheking
solutions do not perform well in correcting typos. We present a data
augmentation method to address the lack of annotated typo data and train a
recurrent neural network to learn context-limited domain-specific embeddings.
Those embeddings are deployed in a real-time inferencing API for the Microsoft
AppSource marketplace to find the closest match between a misspelled user query
and the available product names. Our data efficient solution shows that
controlled high quality synthetic data may be a powerful tool especially
considering the current climate of large language models which rely on
prohibitively huge and often uncontrolled datasets.