@InProceedings{barrett-EtAl:2018:K18-1,
  author    = {Barrett, Maria  and  Bingel, Joachim  and  Hollenstein, Nora  and  Rei, Marek  and  S{\o}gaard, Anders},
  title     = {Sequence Classification with Human Attention},
  booktitle = {Proceedings of the 22nd Conference on Computational Natural Language Learning},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {302--312},
  abstract  = {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. Specifically, we use estimated human attention derived from eye-tracking corpora to regularize attention functions in recurrent neural networks. We show substantial improvements across a range of tasks, including sentiment analysis, grammatical error detection, and detection of abusive language.},
  url       = {http://www.aclweb.org/anthology/K18-1030}
}