Mobile Keyboard Input Decoding with Finite-State Transducers

By Tom Y. Ouyang, David Rybach, Franccoise Beaufays, Michael D. Riley. 2017

32 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Signal Processing

Citations in the corpus

Federated Learning Of Out-Of-Vocabulary Words (2019, background): “Words missing from the vocabulary cannot be predicted on the keyboard suggestion strip, cannot be gesture typed, and more annoyingly, may be autocorrected even when typed correctly (Ouyang et al., 2017).”
Federated Learning for Mobile Keyboard Prediction (2018, background): “FSTs have been explored in the context of mobile keyboard input decoding, correction, and prediction [3].”
Federated Learning of N-Gram Language Models (2019, methodology): “Weighted ﬁnite-state transducers have been used successfully to decode keyboard spatial signals us-ing a combination of spatial and language models (Ouyang et al., 2017; Hellsten et al., 2017).”
Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus (2020, methodology): “…text data, which is more commonly produced, is also used in building out language technologies: for example, in training language models, which are used in many applications ranging from next-word prediction in keyboard input software (Ouyang et al., 2017) to ASR and MT (Buck et al., 2014).”