@InProceedings{augenstein-ruder-sgaard:2018:N18-1,
  author    = {Augenstein, Isabelle  and  Ruder, Sebastian  and  Søgaard, Anders},
  title     = {Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {1896--1906},
  abstract  = {We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state of the art for aspect-based and topic-based sentiment analysis.},
  url       = {http://www.aclweb.org/anthology/N18-1172}
}