104 citations · 183 across the 7 of their papers we have counts for
4 papers · 1 filter
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model
Hao Zhang, You-Chi Cheng, Shankar Kumar +3
Capitalization normalization (truecasing) is the task of restoring the correct case (uppercase or lowercase) of noisy text. We propose a fast, accurate and compact two-level hierar…
Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network
Hao Zhang, You-Chi Cheng, Shankar Kumar +2
Truecasing is the task of restoring the correct case (uppercase or lowercase) of noisy text generated either by an automatic system for speech recognition or machine translation or…
Federated Learning of N-gram Language Models
Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews +4
We propose algorithms to train production-quality n-gram language models using federated learning. Federated learning is a distributed computation platform that can be used to trai…
Federated Learning Of Out-Of-Vocabulary Words
Mingqing Chen, Rajiv Mathews, Tom Ouyang +1
We demonstrate that a character-level recurrent neural network is able to learn out-of-vocabulary (OOV) words under federated learning settings, for the purpose of expanding the vo…