Language modeling for soft keyboards

By Joshua Goodman, Gina Venolia, Keith Steury, Chauncey Parker. 2002

197 citations recorded by the source corpus.

How researchers see this paper

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Practical Secure Aggregation for Federated Learning on User-Held Data (2016, background): “2 Secure Aggregation for Federated Learning Consider training a deep neural network to predict the next word that a user will type as she composes a text message to improve typing accuracy for a phone’s on-screen keyboard [11].”
Gmail Smart Compose: Real-Time Assisted Writing (2019, methodology): “Language models alone can also be used in cases like input method to provide suggestions for subsequent words and to accelerate user typing (see for example [4, 16]).”
Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach (2018, background): “For example, consider the training of a deep Neural Network (NN) model to predict the word that a user will type when composing a text message on its mobile phone [1].”
Neural Networks for Text Correction and Completion in Keyboard Decoding (2017, background): “Some of the classical techniques that dealt with error correction in the literature include combination of probabilistic touch model with character language model [25].”
Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017, methodology): “Such models are commonly used to to improve typing efﬁcacy for a phone’s on-screen keyboard [19].”