This model offers a new perspective where a neural net combines an inference machine and a model (rather than simply encoding a model).
this model represents in its architecture, through a weighted sum of embeddings, the inference procedure encoded in Equation 4 for the Uncertainty Propagation model: where q(z t ) is an embedding that represents the uncertainty at time step t. q(z t ) is computed sequentially as new utterances are received, and used in both decoding and training.