Federated Learning for Emoji Prediction in a Mobile Keyboard

Swaroop Ramaswamy; Rajiv Mathews; Kanishka Rao; Françoise Beaufays

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arXiv · 1906.04329

Federated Learning for Emoji Prediction in a Mobile Keyboard

Swaroop Ramaswamy·Rajiv Mathews·Kanishka Rao·Françoise Beaufays

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Abstract

We show that a word-level recurrent neural network can predict emoji from text typed on a mobile keyboard. We demonstrate the usefulness of transfer learning for predicting emoji by pretraining the model using a language modeling task. We also propose mechanisms to trigger emoji and tune the diversity of candidates. The model is trained using a distributed on-device learning framework called federated learning. The federated model is shown to achieve better performance than a server-trained model. This work demonstrates the feasibility of using federated learning to train production-quality models for natural language understanding tasks while keeping users' data on their devices.

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BibTeX RIS

Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, Françoise Beaufays. 2019-06-11. Federated Learning for Emoji Prediction in a Mobile Keyboard. https://arxiv.org/abs/1906.04329

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