text: We introduce segmental recurrent neural networks (SRNNs) which define, given an input sequence, a joint probability distribution over segmentations of the input and labelings of the segments. Representations of the input segments (i.e., contiguous subsequences of the input) are computed by encoding their constituent tokens using bidirectional recurrent neural nets, and these "segment embeddings" are used to define compatibility scores with output labels. These local compatibility scores are integrated using a global semi-Markov conditional random field. Both fully supervised training -- in which segment boundaries and labels are observed -- as well as partially supervised training -- in which segment boundaries are latent -- are straightforward. Experiments on handwriting recognition and joint Chinese word segmentation POS tagging show that, compared to models that do not explicitly represent segments such as BIO tagging schemes and connectionist temporal classification (CTC), SRNNs obtain substantially higher accuracies.
  @cite_0: Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN.
summary: Segmental labeling problems have been widely studied. A widely used approach to a segmental labeling problems with neural networks is the connectionist temporal classification (CTC) objective and decoding rule of @cite_0 . CTC reduces the segmental'' sequence label problem to a classical sequence labeling problem in which every position in an input sequence @math is explicitly labeled by interpreting repetitions of input labels---or input labels followed by a special blank'' output symbol---as being a single label with a longer duration. During training, the marginal likelihood of the set of labelings compatible (according to the CTC interpretation rules) with the reference label @math is maximized. CTC has demonstrated impressive success in various fully discriminative end-to-end speech recognition models [] graves:2014,maas:2015,hannun:2014 .