@InProceedings{bonadiman-uva-moschitti:2017:EACLshort,
  author    = {Bonadiman, Daniele  and  Uva, Antonio  and  Moschitti, Alessandro},
  title     = {Effective shared representations with Multitask Learning for Community Question Answering},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {726--732},
  abstract  = {An important asset of using Deep Neural Networks (DNNs) for text applications
	is their ability to automatically engineering features.
	Unfortunately, DNNs usually require a lot of training data, especially for
	highly semantic tasks such as community Question Answering (cQA). In this
	paper, we tackle the problem of data scarcity by learning the target DNN
	together with two auxiliary tasks in a multitask learning setting. We exploit
	the strong semantic connection between selection of comments relevant to (i)
	new questions and (ii) forum questions. This enables a global representation
	for comments, new and previous questions.
	The experiments of our model on a SemEval challenge dataset for cQA show a 20%
	of relative improvement over standard DNNs.},
  url       = {http://www.aclweb.org/anthology/E17-2115}
}