Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive bias on many attention functions in NLP.
we use estimated human attention derived from eyetracking corpora to regularize attention functions in recurrent neural networks.