Abstract
Considering diverse aspects of an argumentative issue is an essential step for mitigating a biased opinion and making reasonable decisions. A related generation model can produce flexible results that cover a wide range of topics, compared to the retrieval-based method that may show unstable performance for unseen data. In this paper, we study the problem of generating sentential arguments from multiple perspectives, and propose a neural method to address this problem. Our model, ArgDiver (Argument generation model from diverse perspectives), in a way a conversational system, successfully generates high-quality sentential arguments. At the same time, the automatically generated arguments by our model show a higher diversity than those generated by any other baseline models. We believe that our work provides evidence for the potential of a good generation model in providing diverse perspectives on a controversial topic.- Anthology ID:
- D19-5007
- Volume:
- Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Anna Feldman, Giovanni Da San Martino, Alberto Barrón-Cedeño, Chris Brew, Chris Leberknight, Preslav Nakov
- Venue:
- NLP4IF
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 56–65
- Language:
- URL:
- https://aclanthology.org/D19-5007
- DOI:
- 10.18653/v1/D19-5007
- Cite (ACL):
- ChaeHun Park, Wonsuk Yang, and Jong Park. 2019. Generating Sentential Arguments from Diverse Perspectives on Controversial Topic. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 56–65, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Generating Sentential Arguments from Diverse Perspectives on Controversial Topic (Park et al., NLP4IF 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D19-5007.pdf