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We consider the problem of training input-output recurrent neural networks(RNN) for sequence labeling tasks. We propose a novel spectral approach forlearning the network parameters. It is based on decomposition of thecross-moment tensor between the output and a non-linear transformation of theinput, based on score functions. We guarantee consistent learning withpolynomial sample and computational complexity under transparent conditionssuch as non-degeneracy of model parameters, polynomial activations for theneurons, and a Markovian evolution of the input sequence. We also extend ourresults to Bidirectional RNN which uses both previous and future information tooutput the label at each time point, and is employed in many NLP tasks such asPOS tagging.

Training Input-Output Recurrent Neural Networks through Spectral Methods