Abstract
This paper describes our approach for the automatic grading of evidence task from the Australasian Language Technology Association (ALTA) Shared Task 2021. We developed two classification models with SVM and RoBERTa and applied an ensemble technique to combine the grades from different classifiers. Our results showed that the SVM model achieved comparable results to the RoBERTa model, and the ensemble system outperformed the individual models on this task. Our system achieved the first place among five teams and obtained 3.3% higher accuracy than the second place.- Anthology ID:
- 2021.alta-1.25
- Volume:
- Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association
- Month:
- December
- Year:
- 2021
- Address:
- Online
- Editors:
- Afshin Rahimi, William Lane, Guido Zuccon
- Venue:
- ALTA
- SIG:
- Publisher:
- Australasian Language Technology Association
- Note:
- Pages:
- 213–217
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2021.alta-1.25/
- DOI:
- Cite (ACL):
- Yuting Guo, Yao Ge, Ruqi Liao, and Abeed Sarker. 2021. An Ensemble Model for Automatic Grading of Evidence. In Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association, pages 213–217, Online. Australasian Language Technology Association.
- Cite (Informal):
- An Ensemble Model for Automatic Grading of Evidence (Guo et al., ALTA 2021)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2021.alta-1.25.pdf
- Data
- ALTA 2021 Shared Task