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The stream of words produced by Automatic Speech Recognition (ASR) systems istypically devoid of punctuations and formatting. Most natural languageprocessing applications expect segmented and well-formatted texts as input,which is not available in ASR output. This paper proposes a novel technique ofjointly modeling multiple correlated tasks such as punctuation andcapitalization using bidirectional recurrent neural networks, which leads toimproved performance for each of these tasks. This method could be extended forjoint modeling of any other correlated sequence labeling tasks.

Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks