Mobile Keyboard Input Decoding with Finite-State Transducers
This work addresses input decoding for mobile device users, presenting an incremental adaptation of existing methods to a new domain.
The paper tackled the problem of decoding keyboard inputs on mobile devices by proposing a finite-state transducer (FST) representation, adapting speech recognition techniques to meet memory and latency constraints and enabling features like autocorrections and next word predictions.
We propose a finite-state transducer (FST) representation for the models used to decode keyboard inputs on mobile devices. Drawing from learnings from the field of speech recognition, we describe a decoding framework that can satisfy the strict memory and latency constraints of keyboard input. We extend this framework to support functionalities typically not present in speech recognition, such as literal decoding, autocorrections, word completions, and next word predictions. We describe the general framework of what we call for short the keyboard "FST decoder" as well as the implementation details that are new compared to a speech FST decoder. We demonstrate that the FST decoder enables new UX features such as post-corrections. Finally, we sketch how this decoder can support advanced features such as personalization and contextualization.