@InProceedings{rach-minker-ultes:2017:W17-55,
  author    = {Rach, Niklas  and  Minker, Wolfgang  and  Ultes, Stefan},
  title     = {Interaction Quality Estimation Using Long Short-Term Memories},
  booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},
  month     = {August},
  year      = {2017},
  address   = {Saarbrücken, Germany},
  publisher = {Association for Computational Linguistics},
  pages     = {164--169},
  abstract  = {For estimating the Interaction Quality (IQ) in Spoken Dialogue Systems (SDS),
	the dialogue history is of significant importance. Previous works included this
	information manually in the form of precomputed temporal features into the
	classification process. Here, we employ a deep learning architecture based on
	Long Short-Term Memories (LSTM) to extract this information automatically from
	the data, thus estimating IQ solely by using current exchange features. We show
	that it is thereby possible to achieve competitive results as in a scenario
	where manually optimized temporal features have been included.},
  url       = {http://aclweb.org/anthology/W17-5520}
}