  Accurate spelling correction is a critical step in modern search interfaces,
especially in an era of mobile devices and speech-to-text interfaces. For
services that are deployed around the world, this poses a significant challenge
for multilingual NLP: spelling errors need to be caught and corrected in all
languages, and even in queries that use multiple languages. In this paper, we
tackle this challenge using multi-teacher distillation. On our approach, a
monolingual teacher model is trained for each language/locale, and these
individual models are distilled into a single multilingual student model
intended to serve all languages/locales. In experiments using open-source data
as well as user data from a worldwide search service, we show that this leads
to highly effective spelling correction models that can meet the tight latency
requirements of deployed services.
