4 accepted papers
Millions of multilingual multi-Indic+English users would benefit if mobile text input eschewed manual switching between monolingual transliteration keyboards. We propose to make neural transliteration tagless to enable multilingual inference, and leverage multi-word training to build up a stronger i…
Improving keyboard autocorrection is critical to making textual input on mobile devices faster and more accurate. End-to-end sequence-to-sequence modeling is a promising avenue, but analysis of user revisions reveals that most users are not patient enough to wait until the end of the word for autoco…
As handwriting input becomes more prevalent, the large symbol inventory required to support Chinese handwriting recognition poses unique challenges. This paper describes how the Apple deep learning recognition system can accurately handle up to 30,000 Chinese characters while running in real-time ac…
Continuous path keyboard input has higher inherent ambiguity than standard tapping, because the path trace may exhibit not only local overshoots/undershoots (as in tapping) but also, depending on the user, substantial mid-path excursions. Deploying a robust solution thus requires a large amount of h…