Prefix-Level Detection and Autocorrection of Keyboard Input Errors

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 autocorrection to be performed. Accordingly, this paper proposes a prefix autocorrection framework, in which error detection and correction occurs at the prefix level for a more immediate response. We also introduce a new metric to assess the quality of prefix matching, which more tightly correlates with typo reduction than conventional predictive typing measures. Experiments conducted on large touch sequence datasets suggest that prefix autocorrection is a viable approach to speed up error detection and correction, and that attendant improvements in typo recovery early in the word lead to a smoother keyboard input experience.