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
- Editors:
- Regina Barzilay, Min-Yen Kan
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/P17-2038.pdf