@inproceedings{bexte-etal-2022-similarity,
title = "Similarity-Based Content Scoring - How to Make {S}-{BERT} Keep Up With {BERT}",
author = "Bexte, Marie and
Horbach, Andrea and
Zesch, Torsten",
editor = {Kochmar, Ekaterina and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Madnani, Nitin and
Tack, Ana{\"\i}s and
Yaneva, Victoria and
Yuan, Zheng and
Zesch, Torsten},
booktitle = "Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)",
month = jul,
year = "2022",
address = "Seattle, Washington",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.bea-1.16",
doi = "10.18653/v1/2022.bea-1.16",
pages = "118--123",
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.",
}
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%0 Conference Proceedings
%T Similarity-Based Content Scoring - How to Make S-BERT Keep Up With BERT
%A Bexte, Marie
%A Horbach, Andrea
%A Zesch, Torsten
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F bexte-etal-2022-similarity
%X 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.
%R 10.18653/v1/2022.bea-1.16
%U https://aclanthology.org/2022.bea-1.16
%U https://doi.org/10.18653/v1/2022.bea-1.16
%P 118-123
Markdown (Informal)
[Similarity-Based Content Scoring - How to Make S-BERT Keep Up With BERT](https://aclanthology.org/2022.bea-1.16) (Bexte et al., BEA 2022)
ACL