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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P17-1122.pdf