Contextual Multilingual Spellchecker for User Queries

Sanat Sharma, Josep Valls-Vargas, Tracy Holloway King, Francois Guerin, Chirag Arora

Abstract:

Spellchecking is one of the most fundamental and widely used
search features. Correcting incorrectly spelled user queries not
only enhances the user experience but is expected by the user.
However, most widely available spellchecking solutions are either
lower accuracy than state-of-the-art solutions or too slow to be
used for search use cases where latency is a key requirement. Furthermore,
most innovative recent architectures focus on English
and are not trained in a multilingual fashion and are trained for
spell correction in longer text, which is a different paradigm from
spell correction for user queries, where context is sparse (most
queries are 1–2 words long). Finally, since most enterprises have
unique vocabularies such as product names, off-the-shelf spelling
solutions fall short of users’ needs.
In this work, we build a multilingual spellchecker that is extremely
fast and scalable and that adapts its vocabulary and hence
speller output based on a specific product’s needs. Furthermore our
speller out-performs general purpose spellers by a wide margin on
in-domain datasets. Our multilingual speller is used in search in
Adobe products, powering autocomplete in various applications.