@inproceedings{cagan-etal-2017-data,
    title = "Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation ({ONLG})",
    author = "Cagan, Tomer  and
      Frank, Stefan L.  and
      Tsarfaty, Reut",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P17-1122/",
    doi = "10.18653/v1/P17-1122",
    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."
}