Abstract | ||
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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. |
Year | Venue | DocType |
---|---|---|
2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1906.04329 | 0 | 0.34 |
References | Authors | |
0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Swaroop Ramaswamy | 1 | 0 | 2.70 |
Rajiv Mathews | 2 | 12 | 5.30 |
Kanishka Rao | 3 | 11 | 0.90 |
Françoise Beaufays | 4 | 341 | 27.76 |