Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model (2202.08171v1)

Abstract: 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 hierarchical word-and-character-based recurrent neural network model. We use the truecaser to normalize user-generated text in a Federated Learning framework for LLMing. A case-aware LLM trained on this normalized text achieves the same perplexity as a model trained on text with gold capitalization. In a real user A/B experiment, we demonstrate that the improvement translates to reduced prediction error rates in a virtual keyboard application. Similarly, in an ASR LLM fusion experiment, we show reduction in uppercase character error rate and word error rate.