Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network

Hao Zhang; You-Chi Cheng; Shankar Kumar; Mingqing Chen; Rajiv Mathews

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arXiv · 2108.11943

Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network

Hao Zhang·You-Chi Cheng·Shankar Kumar·Mingqing Chen·Rajiv Mathews

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Abstract

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 by humans. It improves the performance of downstream NLP tasks such as named entity recognition and language modeling. We propose a fast, accurate and compact two-level hierarchical word-and-character-based recurrent neural network model, the first of its kind for this problem. Using sequence distillation, we also address the problem of truecasing while ignoring token positions in the sentence, i.e. in a position-invariant manner.

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Hao Zhang, You-Chi Cheng, Shankar Kumar, Mingqing Chen, Rajiv Mathews. 2021-08-26. Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network. https://arxiv.org/abs/2108.11943

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