@InProceedings{tiennguyen-joty:2017:Long,
  author    = {Tien Nguyen, Dat  and  Joty, Shafiq},
  title     = {A Neural Local Coherence Model},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1320--1330},
  abstract  = {We propose a local coherence model based on a convolutional neural network that
	operates over the entity grid representation of a text. The model captures long
	range en- tity transitions along with entity-specific features without loosing
	generalization, thanks to the power of distributed representation. We present a
	pairwise ranking method to train the model in an end-to-end fashion on a task
	and learn task-specific high level features. Our evaluation on three different
	coherence assessment tasks demonstrates that our model achieves state of the
	art results outperforming existing models by a good margin.},
  url       = {http://aclweb.org/anthology/P17-1121}
}