@InProceedings{dai-huang:2018:N18-1,
  author    = {Dai, Zeyu  and  Huang, Ruihong},
  title     = {Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph},
  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     = {141--151},
  abstract  = {We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph. Experimental results show that our model outperforms the previous state-of-the-art systems on the benchmark corpus of PDTB.},
  url       = {http://www.aclweb.org/anthology/N18-1013}
}