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Slide 36 text
Skip Thought Architecture
● Then, an encoder, built using recurrent neural network layers(GRU),is
able to capture the patterns of sequential word vectors. The hidden
states of the encoder are fed as representations of the inputs into two
separate decoders (to predict the preceding and subsequent sentence)
● Intuitively speaking, the encoder generates a representation of the
input sentence itself. Back-propagating costs from the decoder during
training enables the encoder to capture the relationship of the input
sentence to its surrounding sentences as well.
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