PAPIs 2018

In order to build robust NLP models that can reason from language well, architectures should function more similarly to how our human brains work over pure pattern recognition. Attention is an interpretable type of neural network layer that is loosely based on attention in humans, and it has recently enabled a powerful alternative to RNNs. Attention-based models have produced new techniques and state of the art performances for many language modeling tasks. In this presentation, an introduction to Attention layers will be given along with why and how they have been utilized to revolutionize NLP.