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Capitalization normalization (truecasing) is the task of restoring thecorrect case (uppercase or lowercase) of noisy text. We propose a fast,accurate and compact two-level hierarchical word-and-character-based recurrentneural network model. We use the truecaser to normalize user-generated text ina Federated Learning framework for language modeling. A case-aware languagemodel trained on this normalized text achieves the same perplexity as a modeltrained on text with gold capitalization. In a real user A/B experiment, wedemonstrate that the improvement translates to reduced prediction error ratesin a virtual keyboard application. Similarly, in an ASR language model fusionexperiment, we show reduction in uppercase character error rate and word errorrate.

Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model