@inproceedings{yasser-etal-2025-averroes,
title = "Averroes at {BEA} 2025 Shared Task: Verifying Mistake Identification in Tutor, Student Dialogue",
author = "Yasser, Mazen and
Saeed, Mariam and
Elkordi, Hossam and
Khalafallah, Ayman",
editor = {Kochmar, Ekaterina and
Alhafni, Bashar and
Bexte, Marie and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Tack, Ana{\"i}s and
Yaneva, Victoria and
Yuan, Zheng},
booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.bea-1.87/",
doi = "10.18653/v1/2025.bea-1.87",
pages = "1121--1126",
ISBN = "979-8-89176-270-1",
abstract = "This paper presents the approach and findings of Averroes Team in the BEA 2025 Shared Task Track 1: Mistake Identification. Our system uses the multilingual understanding capabilities of general text embedding models. Our approach involves full-model fine-tuning, where both the pre-trained language model and the classification head are optimized to detect tutor recognition of student mistakes in educational dialogues. This end-to-end training enables the model to better capture subtle pedagogical cues, leading to improved contextual understanding. Evaluated on the official test set, our system achieved an exact macro-F{\_}1 score of 0.7155 and an accuracy of 0.8675, securing third place among the participating teams. These results underline the effectiveness of task-specific optimization in enhancing model sensitivity to error recognition within interactive learning contexts."
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%0 Conference Proceedings
%T Averroes at BEA 2025 Shared Task: Verifying Mistake Identification in Tutor, Student Dialogue
%A Yasser, Mazen
%A Saeed, Mariam
%A Elkordi, Hossam
%A Khalafallah, Ayman
%Y Kochmar, Ekaterina
%Y Alhafni, Bashar
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-270-1
%F yasser-etal-2025-averroes
%X This paper presents the approach and findings of Averroes Team in the BEA 2025 Shared Task Track 1: Mistake Identification. Our system uses the multilingual understanding capabilities of general text embedding models. Our approach involves full-model fine-tuning, where both the pre-trained language model and the classification head are optimized to detect tutor recognition of student mistakes in educational dialogues. This end-to-end training enables the model to better capture subtle pedagogical cues, leading to improved contextual understanding. Evaluated on the official test set, our system achieved an exact macro-F_1 score of 0.7155 and an accuracy of 0.8675, securing third place among the participating teams. These results underline the effectiveness of task-specific optimization in enhancing model sensitivity to error recognition within interactive learning contexts.
%R 10.18653/v1/2025.bea-1.87
%U https://aclanthology.org/2025.bea-1.87/
%U https://doi.org/10.18653/v1/2025.bea-1.87
%P 1121-1126
Markdown (Informal)
[Averroes at BEA 2025 Shared Task: Verifying Mistake Identification in Tutor, Student Dialogue](https://aclanthology.org/2025.bea-1.87/) (Yasser et al., BEA 2025)
ACL