@InProceedings{perezbeltrachini-sayed-gardent:2016:COLING,
  author    = {Perez-Beltrachini, Laura  and  SAYED, Rania  and  Gardent, Claire},
  title     = {Building RDF Content for Data-to-Text Generation},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1493--1502},
  abstract  = {In Natural Language Generation (NLG), one important limitation is the lack of
	common benchmarks on which to train, evaluate and compare data-to-text
	generators. In this paper, we make one step in that direction and introduce a
	method for automatically creating an arbitrary large repertoire of data units
	that could serve as input for generation.  Using both automated metrics and a
	human evaluation, we show that the data units produced by our method are both
	diverse and coherent.},
  url       = {http://aclweb.org/anthology/C16-1141}
}