@InProceedings{byamugisha-keet-derenzi:2017:INLG20171,
  author    = {Byamugisha, Joan  and  Keet, C. Maria  and  DeRenzi, Brian},
  title     = {Evaluation of a Runyankore grammar engine for healthcare messages},
  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     = {105--113},
  abstract  = {Natural Language Generation (NLG) can be used to generate personalized health
	information, which is especially useful when provided in one's own language.
	However, the NLG technique widely used in different domains and
	languages---templates---was shown to be inapplicable to Bantu languages, due to
	their characteristic agglutinative structure. We present here our use of the
	grammar engine NLG technique to generate text in Runyankore, a Bantu language
	indigenous to Uganda. Our grammar engine adds to previous work in this field
	with new rules for cardinality constraints, prepositions in roles, the passive,
	and phonological conditioning. We evaluated the generated text with linguists
	and non-linguists, who regarded most text as grammatically correct and
	understandable; and over 60\% of them regarded all the text generated by our
	system to have been authored by a human being.},
  url       = {http://www.aclweb.org/anthology/W17-3514}
}