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Learning to encode spatial relations from natural language
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.
Variational Smoothing in Recurrent Neural Network Language Models
We present a new theoretical perspective of data noising in recurrent neural network language models (Xie et al., 2017).
Memory Architectures in Recurrent Neural Network Language Models
We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models.
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