  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.
