Abstract
The dominating paradigm for content scoring is to learn an instance-based model, i.e. to use lexical features derived from the learner answers themselves. An alternative approach that receives much less attention is however to learn a similarity-based model. We introduce an architecture that efficiently learns a similarity model and find that results on the standard ASAP dataset are on par with a BERT-based classification approach.- Anthology ID:
- 2022.bea-1.16
- Volume:
- Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, Washington
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 118–123
- Language:
- URL:
- https://aclanthology.org/2022.bea-1.16
- DOI:
- 10.18653/v1/2022.bea-1.16
- Cite (ACL):
- Marie Bexte, Andrea Horbach, and Torsten Zesch. 2022. Similarity-Based Content Scoring - How to Make S-BERT Keep Up With BERT. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), pages 118–123, Seattle, Washington. Association for Computational Linguistics.
- Cite (Informal):
- Similarity-Based Content Scoring - How to Make S-BERT Keep Up With BERT (Bexte et al., BEA 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.bea-1.16.pdf
- Code
- mariebexte/s-bert-similarity-based-content-scoring