  We propose a segmental neural language model that combines the generalization
power of neural networks with the ability to discover word-like units that are
latent in unsegmented character sequences. In contrast to previous segmentation
models that treat word segmentation as an isolated task, our model unifies word
discovery, learning how words fit together to form sentences, and, by
conditioning the model on visual context, how words' meanings ground in
representations of non-linguistic modalities. Experiments show that the
unconditional model learns predictive distributions better than character LSTM
models, discovers words competitively with nonparametric Bayesian word
segmentation models, and that modeling language conditional on visual context
improves performance on both.
