Abstract
Exchanging arguments is an important part in communication, but we are often flooded with lots of arguments for different positions or are captured in filter bubbles. Tools which can present strong arguments relevant to oneself could help to reduce those problems. To be able to evaluate algorithms which can predict how convincing an argument is, we have collected a dataset with more than 900 arguments and personal attitudes of 600 individuals, which we present in this paper. Based on this data, we suggest three recommender tasks, for which we provide two baseline results from a simple majority classifier and a more complex nearest-neighbor algorithm. Our results suggest that better algorithms can still be developed, and we invite the community to improve on our results.- Anthology ID:
- 2021.sigdial-1.38
- Volume:
- Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- July
- Year:
- 2021
- Address:
- Singapore and Online
- Editors:
- Haizhou Li, Gina-Anne Levow, Zhou Yu, Chitralekha Gupta, Berrak Sisman, Siqi Cai, David Vandyke, Nina Dethlefs, Yan Wu, Junyi Jessy Li
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 360–367
- Language:
- URL:
- https://aclanthology.org/2021.sigdial-1.38
- DOI:
- 10.18653/v1/2021.sigdial-1.38
- Cite (ACL):
- Markus Brenneis, Maike Behrendt, and Stefan Harmeling. 2021. How Will I Argue? A Dataset for Evaluating Recommender Systems for Argumentations. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 360–367, Singapore and Online. Association for Computational Linguistics.
- Cite (Informal):
- How Will I Argue? A Dataset for Evaluating Recommender Systems for Argumentations (Brenneis et al., SIGDIAL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.sigdial-1.38.pdf
- Code
- hhucn/argumentation-attitude-dataset