Detecting Good Arguments in a Non-Topic-Specific Way: An Oxymoron?

Beata Beigman Klebanov, Binod Gyawali, Yi Song


Abstract
Automatic identification of good arguments on a controversial topic has applications in civics and education, to name a few. While in the civics context it might be acceptable to create separate models for each topic, in the context of scoring of students’ writing there is a preference for a single model that applies to all responses. Given that good arguments for one topic are likely to be irrelevant for another, is a single model for detecting good arguments a contradiction in terms? We investigate the extent to which it is possible to close the performance gap between topic-specific and across-topics models for identification of good arguments.
Anthology ID:
P17-2038
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
244–249
Language:
URL:
https://aclanthology.org/P17-2038
DOI:
10.18653/v1/P17-2038
Bibkey:
Cite (ACL):
Beata Beigman Klebanov, Binod Gyawali, and Yi Song. 2017. Detecting Good Arguments in a Non-Topic-Specific Way: An Oxymoron?. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 244–249, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Detecting Good Arguments in a Non-Topic-Specific Way: An Oxymoron? (Beigman Klebanov et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2038.pdf