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Truecasing is the task of restoring the correct case (uppercase or lowercase)of noisy text generated either by an automatic system for speech recognition ormachine translation or by humans. It improves the performance of downstream NLPtasks such as named entity recognition and language modeling. We propose afast, accurate and compact two-level hierarchical word-and-character-basedrecurrent neural network model, the first of its kind for this problem. Usingsequence distillation, we also address the problem of truecasing while ignoringtoken positions in the sentence, i.e. in a position-invariant manner.

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