@InProceedings{castroferreira-EtAl:2018:Long,
  author    = {Castro Ferreira, Thiago  and  Moussallem, Diego  and  Kádár, Ákos  and  Wubben, Sander  and  Krahmer, Emiel},
  title     = {NeuralREG: An end-to-end approach to referring expression generation},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {1959--1969},
  abstract  = {Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function. In this paper, we present a new approach (NeuralREG), relying on deep neural networks, which makes decisions about form and content in one go without explicit feature extraction. Using a delexicalized version of the WebNLG corpus, we show that the neural model substantially improves over two strong baselines.},
  url       = {http://www.aclweb.org/anthology/P18-1182}
}