用原型锚定与主题无关归一化，提升阿拉伯语立场检测效果
PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection
- 将立场识别转为填空式语言建模，用可学习词元映射标签
- 在两个子任务上分别取得0.75和0.74的宏平均F1分数
- 适合低资源场景下的阿拉伯语立场分析，架构改动极小
我们提出PAST-TIDE，用于应对NakbaNLP@LREC-COLING 2026中StanceNakba共享任务的两个子任务。核心思想是陈述调优：将立场定义为填空式的掩码语言建模（MLM），通过预训练的MLM头部，让一个词元生成器将标签词映射到立场类别，而非添加随机初始化的分类头。同时引入原型对比学习，使用可学习的类别原型实现与批量大小无关的对比训练，并采用主题条件层归一化，提升跨主题阿拉伯语立场检测性能。PAST-TIDE在官方排行榜上，子任务A和子任务B的宏平均F1分别为0.75和0.74，表明对预训练模型进行最小架构修改，仍可在低资源场景下保持竞争力。
原文摘要 · Abstract (English)
We introduce PAST-TIDE, our stance detection system addressing both subtasks of the StanceNakba Shared Task at NakbaNLP@LREC-COLING 2026. The main idea is statement tuning. We redefine stance as cloze-style masked language modeling (MLM), letting a verbalizer map label words to stance categories through the pre-trained MLM head rather than appending a randomly initialized classification head. We complement this with prototypical contrastive learning, which uses learnable class prototypes for batch-size independent contrastive training, and topic-conditional layer normalization for cross-topic Arabic stance detection. PAST-TIDE achieves macro-F1 scores of 0.75 for Subtask A and 0.74 for Subtask B on the official leaderboard, indicating that minimal architectural additions to a pre-trained model can remain competitive in low-resource settings.
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