@InProceedings{subramanian-cohn-baldwin:2018:N18-1,
  author    = {Subramanian, Shivashankar  and  Cohn, Trevor  and  Baldwin, Timothy},
  title     = {Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis},
  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     = {1964--1974},
  abstract  = {Election manifestos document the intentions, motives, and views of political parties. They are often used for analysing a party's fine-grained position on a particular issue, as well as for coarse-grained positioning of a party on the left--right spectrum. In this paper we propose a two-stage model for automatically performing both levels of analysis over manifestos. In the first step we employ a hierarchical multi-task structured deep model to predict fine- and coarse-grained positions, and in the second step we perform post-hoc calibration of coarse-grained positions using probabilistic soft logic. We empirically show that the proposed model outperforms state-of-art approaches at both granularities using manifestos from twelve countries, written in ten different languages.},
  url       = {http://www.aclweb.org/anthology/N18-1178}
}