@inproceedings{fiacco-etal-2023-towards,
title = "Towards Extracting and Understanding the Implicit Rubrics of Transformer Based Automatic Essay Scoring Models",
author = "Fiacco, James and
Adamson, David and
Rose, Carolyn",
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 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bea-1.20",
doi = "10.18653/v1/2023.bea-1.20",
pages = "232--241",
abstract = "By aligning the functional components derived from the activations of transformer models trained for AES with external knowledge such as human-understandable feature groups, the proposed method improves the interpretability of a Longformer Automatic Essay Scoring (AES) system and provides tools for performing such analyses on further neural AES systems. The analysis focuses on models trained to score essays based on organization, main idea, support, and language. The findings provide insights into the models{'} decision-making processes, biases, and limitations, contributing to the development of more transparent and reliable AES systems.",
}
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%0 Conference Proceedings
%T Towards Extracting and Understanding the Implicit Rubrics of Transformer Based Automatic Essay Scoring Models
%A Fiacco, James
%A Adamson, David
%A Rose, Carolyn
%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 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F fiacco-etal-2023-towards
%X By aligning the functional components derived from the activations of transformer models trained for AES with external knowledge such as human-understandable feature groups, the proposed method improves the interpretability of a Longformer Automatic Essay Scoring (AES) system and provides tools for performing such analyses on further neural AES systems. The analysis focuses on models trained to score essays based on organization, main idea, support, and language. The findings provide insights into the models’ decision-making processes, biases, and limitations, contributing to the development of more transparent and reliable AES systems.
%R 10.18653/v1/2023.bea-1.20
%U https://aclanthology.org/2023.bea-1.20
%U https://doi.org/10.18653/v1/2023.bea-1.20
%P 232-241
Markdown (Informal)
[Towards Extracting and Understanding the Implicit Rubrics of Transformer Based Automatic Essay Scoring Models](https://aclanthology.org/2023.bea-1.20) (Fiacco et al., BEA 2023)
ACL