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Living a discrete life in a continuous world: Reference with distributed representations

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(2017)cite arxiv:1702.01815Comment: Under review at ACL 2017. 6 pages, 1 figure.

Аннотация

Reference is the crucial property of language that allows us to connect linguistic expressions to the world. Modeling it requires handling both continuous and discrete aspects of meaning. Data-driven models excel at the former, but struggle with the latter, and the reverse is true for symbolic models. We propose a fully data-driven, end-to-end trainable model that, while operating on continuous multimodal representations, learns to organize them into a discrete-like entity library. We also introduce a referential task to test it, cross-modal tracking. Our model beats standard neural network architectures, but is outperformed by some parametrizations of Memory Networks, another model with external memory.

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