发表机构
Indian Institute of Information Technology, Design and Manufacturing, Kurnool（印度库努尔信息技术、设计与制造学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
BioSentinel团队参与EXIST 2026任务2.2，用基于xlm-roberta-base的文本中心方法，结合复合损失函数训练，在表情包性别歧视意图分类任务中，给出硬标签和软标签预测，通过消融等分析得出相关结论，在测试集取得一定排名。
AI 中文摘要
本文描述了BioSentinel团队参与EXIST 2026任务2.2：表情包中的来源意图，这是CLEF 2026评估活动的一部分。该任务要求在学习不一致（Le-Wi-Di）范式下，将表情包背后的交流意图分类为直接、评判性或无（非性别歧视），该范式要求硬标签和软标签（概率分布）预测。我们提出了一种以文本为中心的方法，基于xlm-roberta-base（2.7亿参数）训练，使用复合损失函数，结合软注释器分布上的KL散度和硬标签上的加权交叉熵。在官方测试集上，该系统实现了ICM-Soft-Norm为0.3229和ICM-Norm为0.3778，硬F1分数为0.4236，在软-软评估中排名第40（共118份提交），在硬-硬评估中排名第49（共187份提交）。我们对数据集特征、探索性更大架构运行以及注释器分歧在主观NLP任务模型设计中的作用进行了分析。消融结果表明，KL损失提高了软标签指标，而CE损失提高了硬标签准确性。我们还报告了单独的验证集温度分析。
英文摘要
This paper describes the BioSentinel team's participation in EXIST 2026 Task 2.2: Source Intention in Memes, part of the CLEF 2026 evaluation campaign. The task requires classifying the communicative intent behind memes as direct, judgemental, or no (non-sexist), under a Learning with Disagreement (Le-Wi-Di) paradigm that mandates both hard-label and soft-label (probability distribution) predictions. We present a text-centric approach built on xlm-roberta-base (270M parameters) trained with a composite loss function combining KL divergence on soft annotator distributions and weighted cross-entropy on hard labels. On the official test set, the system achieved an ICM-Soft-Norm of 0.3229 and ICM-Norm of 0.3778, with a hard F1-score of 0.4236, ranking 40th (out of 118 submissions) in the soft-soft evaluation and 49th (out of 187 submissions) in the hard-hard evaluation. We provide an analysis of the dataset characteristics, exploratory larger-architecture runs, and the role of annotator disagreement in shaping model design for subjective NLP tasks. Ablation results show that KL loss improves soft-label metrics, while CE loss improves hard-label accuracy. We also report a separate validation-set temperature analysis.
Comments9 pages, 1 figure, 6 tables. Accepted for publication in the CLEF 2026 Working Notes (EXIST 2026), Jena, Germany
