Mobile Keyboard Input Decoding with Finite-State Transducers

TL;DR

This paper introduces a finite-state transducer framework for mobile keyboard input decoding, enabling efficient, low-latency processing and supporting advanced features like autocorrection, word completion, and personalization.

Contribution

It adapts speech recognition FST techniques to mobile keyboards, incorporating new functionalities and implementation details for improved user experience.

Findings

Supports strict memory and latency constraints

Enables features like autocorrection and word prediction

Facilitates personalization and contextualization

Abstract

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.