@InProceedings{gonzalezgarduno-sogaard:2017:BEA,
  author    = {Gonzalez-Gardu\~{n}o, Ana Valeria  and  S{\o}gaard, Anders},
  title     = {Using Gaze to Predict Text Readability},
  booktitle = {Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {438--443},
  abstract  = {We show that text readability prediction improves significantly from hard
	parameter sharing with models predicting first pass duration, total fixation
	duration and regression duration. Specifically, we induce multi-task Multilayer
	Perceptrons and Logistic Regression models over sentence representations that
	capture various aggregate statistics, from two different text readability
	corpora for English, as well as the Dundee eye-tracking corpus. Our approach
	leads to significant improvements over Single task learning and over previous
	systems. In addition, our improvements are consistent across train sample
	sizes, making our approach especially applicable to small datasets.},
  url       = {http://www.aclweb.org/anthology/W17-5050}
}