Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data
Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such task
hey require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited