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Representation of linguistic form and function in recurrent neural networks
[article]
2016
arXiv
pre-print
We present novel methods for analyzing the activation patterns of RNNs from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a multi-task gated recurrent network architecture consisting of two parallel pathways with shared word embeddings trained on predicting the representations of the visual scene corresponding to an input sentence, and predicting the next word in the same sentence. Based on our proposed method to estimate the amount
arXiv:1602.08952v2
fatcat:6u4hpmx6ffbwbe452ogfwg3yma