@InProceedings{williams-scheutz:2017:INLG2017,
  author    = {Williams, Tom  and  Scheutz, Matthias},
  title     = {Referring Expression Generation under Uncertainty: Algorithm and Evaluation Framework},
  booktitle = {Proceedings of the 10th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2017},
  address   = {Santiago de Compostela, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {75--84},
  abstract  = {For situated agents to effectively engage in natural-language interactions with
	humans, they must be able to refer to entities such as people, locations, and
	objects. While classic referring expression generation (REG) algorithms like
	the Incremental Algorithm (IA) assume perfect, complete, and accessible
	knowledge of all referents, this is not always possible. In this work, we show
	how a previously presented consultant framework (which facilitates reference
	resolution when knowledge is uncertain, heterogeneous and distributed) can be
	used to extend the IA to produce DIST-PIA, a domain-independent algorithm for
	REG under uncertain, heterogeneous, and distributed knowledge. We also present
	a novel framework that can be used to evaluate such REG algorithms without
	conflating the performance of the algorithm with the performance of classifiers
	it employs.},
  url       = {http://www.aclweb.org/anthology/W17-3511}
}