@inproceedings{chen-etal-2025-sysupporter,
title = "{SYSU}pporter Team at {BEA} 2025 Shared Task: Class Compensation and Assignment Optimization for {LLM}-generated Tutor Identification",
author = "Chen, Longfeng and
Huang, Zeyu and
Xiao, Zheng and
Zeng, Yawen and
Xu, Jin",
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-1.83/",
doi = "10.18653/v1/2025.bea-1.83",
pages = "1078--1083",
ISBN = "979-8-89176-270-1",
abstract = "In this paper, we propose a novel framework for the tutor identification track of the BEA 2025 shared task (Track 5). Our framework integrates data-algorithm co-design, dynamic class compensation, and structured prediction optimization. Specifically, our approach employs noise augmentation, a fine-tuned DeBERTa-v3-small model with inverse-frequency weighted loss, and Hungarian algorithm-based label assignment to address key challenges, such as severe class imbalance and variable-length dialogue complexity. Our method achieved 0.969 Macro-F1 score on the official test set, securing second place in this competition. Ablation studies revealed significant improvements: a 9.4{\%} gain in robustness from data augmentation, a 5.3{\%} boost in minority-class recall thanks to the weighted loss, and a 2.1{\%} increase in Macro-F1 score through Hungarian optimization. This work advances the field of educational AI by providing a solution for tutor identification, with implications for quality control in LLM-assisted learning environments."
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<abstract>In this paper, we propose a novel framework for the tutor identification track of the BEA 2025 shared task (Track 5). Our framework integrates data-algorithm co-design, dynamic class compensation, and structured prediction optimization. Specifically, our approach employs noise augmentation, a fine-tuned DeBERTa-v3-small model with inverse-frequency weighted loss, and Hungarian algorithm-based label assignment to address key challenges, such as severe class imbalance and variable-length dialogue complexity. Our method achieved 0.969 Macro-F1 score on the official test set, securing second place in this competition. Ablation studies revealed significant improvements: a 9.4% gain in robustness from data augmentation, a 5.3% boost in minority-class recall thanks to the weighted loss, and a 2.1% increase in Macro-F1 score through Hungarian optimization. This work advances the field of educational AI by providing a solution for tutor identification, with implications for quality control in LLM-assisted learning environments.</abstract>
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%0 Conference Proceedings
%T SYSUpporter Team at BEA 2025 Shared Task: Class Compensation and Assignment Optimization for LLM-generated Tutor Identification
%A Chen, Longfeng
%A Huang, Zeyu
%A Xiao, Zheng
%A Zeng, Yawen
%A Xu, Jin
%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 chen-etal-2025-sysupporter
%X In this paper, we propose a novel framework for the tutor identification track of the BEA 2025 shared task (Track 5). Our framework integrates data-algorithm co-design, dynamic class compensation, and structured prediction optimization. Specifically, our approach employs noise augmentation, a fine-tuned DeBERTa-v3-small model with inverse-frequency weighted loss, and Hungarian algorithm-based label assignment to address key challenges, such as severe class imbalance and variable-length dialogue complexity. Our method achieved 0.969 Macro-F1 score on the official test set, securing second place in this competition. Ablation studies revealed significant improvements: a 9.4% gain in robustness from data augmentation, a 5.3% boost in minority-class recall thanks to the weighted loss, and a 2.1% increase in Macro-F1 score through Hungarian optimization. This work advances the field of educational AI by providing a solution for tutor identification, with implications for quality control in LLM-assisted learning environments.
%R 10.18653/v1/2025.bea-1.83
%U https://aclanthology.org/2025.bea-1.83/
%U https://doi.org/10.18653/v1/2025.bea-1.83
%P 1078-1083
Markdown (Informal)
[SYSUpporter Team at BEA 2025 Shared Task: Class Compensation and Assignment Optimization for LLM-generated Tutor Identification](https://aclanthology.org/2025.bea-1.83/) (Chen et al., BEA 2025)
ACL