@InProceedings{joty-EtAl:2017:CoNLL,
  author    = {Joty, Shafiq  and  Nakov, Preslav  and  M\`{a}rquez, Llu\'{i}s  and  Jaradat, Israa},
  title     = {Cross-language Learning with Adversarial Neural Networks},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {226--237},
  abstract  = {We address the problem of cross-language adaptation for question-question
	similarity reranking in community question answering, with the objective to
	port a system trained on one input language to another input language given
	labeled training data for the first language and only unlabeled data for the
	second language.
	In particular, we propose to use adversarial training of neural networks to
	learn high-level features that are discriminative for the main learning task,
	and at the same time are invariant across the input languages. The evaluation
	results show sizable improvements for our cross-language adversarial neural
	network (CLANN) model over a strong non-adversarial system.},
  url       = {http://aclweb.org/anthology/K17-1024}
}