Similarity-Based Content Scoring - How to Make S-BERT Keep Up With BERT

Marie Bexte, Andrea Horbach, Torsten Zesch


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
Venues:
BEA | NAACL
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.bea-1.16.pdf
Code
 mariebexte/s-bert-similarity-based-content-scoring