Text Normalization using Memory Augmented Neural Networks
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We propose a memory augmented neural network to perform text normalization i.e. the transformation of words from the written to the spoken form. With the addition of dynamic memory access and storage mechanism, we present an architecture that will serve as a language agnostic text normalization system while avoiding the kind of silly errors made by the LSTM based recurrent neural architectures. By reducing the number of unacceptable mistakes, we show that such a novel architecture is indeed a better alternative. Our proposed system requires significantly lesser amounts of data, training time and compute resources. However, some occurrences of errors still remain in certain semiotic classes. Nevertheless, we demonstrate that memory augmented networks with meta-learning capabilities can open many doors to a superior text normalization system.
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