Jae Ro


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Scaling Language Model Size in Cross-Device Federated Learning
Jae Ro | Theresa Breiner | Lara McConnaughey | Mingqing Chen | Ananda Suresh | Shankar Kumar | Rajiv Mathews
Proceedings of the First Workshop on Federated Learning for Natural Language Processing (FL4NLP 2022)

Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks. In this work, we leverage various techniques for mitigating these bottlenecks to train larger language models in cross-device federated learning. With systematic applications of partial model training, quantization, efficient transfer learning, and communication-efficient optimizers, we are able to train a 21M parameter Transformer that achieves the same perplexity as that of a similarly sized LSTM with ∼10× smaller client-to-server communication cost and 11% lower perplexity than smaller LSTMs commonly studied in literature.


Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities
Hao Zhang | Jae Ro | Richard Sproat
Proceedings of the 28th International Conference on Computational Linguistics

Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search. We link this problem to the classic problem of Chinese word segmentation and show the effectiveness of a tagging model based on Recurrent Neural Networks (RNNs) using characters as input. To compensate for the lack of training data, we propose a pre-training method on concatenated entity names in a large knowledge database. Pre-training improves the model by 33% and brings the sequence accuracy to 85%.