@InProceedings{dligach-EtAl:2017:EACLshort,
  author    = {Dligach, Dmitriy  and  Miller, Timothy  and  Lin, Chen  and  Bethard, Steven  and  Savova, Guergana},
  title     = {Neural Temporal Relation Extraction},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {746--751},
  abstract  = {We experiment with neural architectures for temporal relation extraction and
	establish a new state-of-the-art for several scenarios. We find that neural
	models with only tokens as input outperform state-of-the-art hand-engineered
	feature-based models, that convolutional neural networks outperform LSTM
	models, and
	that encoding relation arguments with XML tags outperforms a traditional
	position-based encoding.},
  url       = {http://www.aclweb.org/anthology/E17-2118}
}