@InProceedings{cagan-frank-tsarfaty:2017:Long,
  author    = {Cagan, Tomer  and  Frank, Stefan L.  and  Tsarfaty, Reut},
  title     = {Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1331--1341},
  abstract  = {Opinionated Natural Language Generation (ONLG) is a new, challenging, task that
	aims to automatically generate human-like, subjective, responses to opinionated
	articles online. 
	We present a data-driven architecture for ONLG that generates subjective
	responses triggered by users’ agendas, consisting of topics and sentiments,
	and based on wide-coverage automatically-acquired generative grammars.
	We compare three types of grammatical representations that we design for ONLG,
	which interleave different layers of linguistic information and are induced
	from a new, enriched dataset we developed.
	Our evaluation shows that generation with Relational-Realizational (Tsarfaty
	and Sima’an, 2008) inspired grammar gets better language model scores than
	lexicalized grammars `a la Collins (2003), and that the latter gets better
	human-evaluation scores. 
	We also show that conditioning the generation on topic models makes generated
	responses more relevant to the document content.},
  url       = {http://aclweb.org/anthology/P17-1122}
}