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

• URL: http://arxiv.org/abs/2108.11943v1
• Date: Thu, 26 Aug 2021 17:54:35 GMT
• Title: Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network
• Authors: Hao Zhang and You-Chi Cheng and Shankar Kumar and Mingqing Chen and Rajiv Mathews
• Abstract summary: We propose a fast, accurate and compact two-level hierarchical word-and-character-based recurrent neural network model. We also address the problem of truecasing while ignoring token positions in the sentence.
• Score: 10.425277173548212
• License: http://creativecommons.org/licenses/by/4.0/
• 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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