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We propose a segmental neural language model that combines the generalizationpower of neural networks with the ability to discover word-like units that arelatent in unsegmented character sequences. In contrast to previous segmentationmodels that treat word segmentation as an isolated task, our model unifies worddiscovery, learning how words fit together to form sentences, and, byconditioning the model on visual context, how words' meanings ground inrepresentations of non-linguistic modalities. Experiments show that theunconditional model learns predictive distributions better than character LSTMmodels, discovers words competitively with nonparametric Bayesian wordsegmentation models, and that modeling language conditional on visual contextimproves performance on both.

Learning to Discover, Ground and Use Words with Segmental Neural Language Models