@InProceedings{ircing-EtAl:2017:BEA,
  author    = {Ircing, Pavel  and  Svec, Jan  and  Zajic, Zbynek  and  Hladka, Barbora  and  Holub, Martin},
  title     = {Combining Textual and Speech Features in the NLI Task Using State-of-the-Art Machine Learning Techniques},
  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     = {198--209},
  abstract  = {We summarize the involvement of our CEMI team in the ''NLI Shared Task 2017'',
	which deals with both textual and speech input data. We submitted the results
	achieved by using three different system architectures; each of them combines
	multiple supervised learning models trained on various feature sets. As
	expected, better results are achieved with the systems that use both the
	textual data and the spoken responses. Combining the input data of two
	different modalities led to a rather dramatic improvement in classification
	performance. 
	Our best performing method is based on a set of feed-forward neural networks
	whose hidden-layer outputs are combined together using a softmax layer. We
	achieved a macro-averaged F1 score of 0.9257 on the evaluation (unseen) test
	set and our team placed first in the main task together with other three teams.},
  url       = {http://www.aclweb.org/anthology/W17-5021}
}