The ﬁrst and most common approach is the interaction between the input x and each hidden unit vector h(t)is parametrized by a newly introduced weight matrix R that was absent from the model of only the sequence of y values


University of Michigan - Ann Arbor

"Conditioning" in the context of sequence to sequence learning in RNNs is the process of computing the probability of obtaining the output sequence conditioned on the input sequence, or p(y|x). The network is used to model this conditional probability mapping

We can extend a model to represent a distribution P(y|x) by using 3 common methods:

* As an extra input at each time step, or
* As the initial state h(0), or
* Both


Modeling Sequences Conditioned on Context with RNNs

Goodfellow, I., Bengio, Y., & Courville, A. (2016). $\mathit{Deep \ Learning.}$ MIT Press. Retrieved from [www.deeplearningbook.org](https://www.deeplearningbook.org) 

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