Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)

Tomer Cagan, Stefan L. Frank, Reut Tsarfaty


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.
Anthology ID:
P17-1122
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1331–1341
Language:
URL:
https://aclanthology.org/P17-1122
DOI:
10.18653/v1/P17-1122
Bibkey:
Cite (ACL):
Tomer Cagan, Stefan L. Frank, and Reut Tsarfaty. 2017. Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1331–1341, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG) (Cagan et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1122.pdf
Note:
 P17-1122.Notes.pdf