| title | PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection |
|---|---|
| authors | Md. Shakhoyat Rahman Shujon, MD Jahid Hasan Jim, Md. Milon Islam, Md Rezwanul Haque, Fakhri Karray |
| date | 2026-07-06 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.04690 |
| links | abs pdf hf code |
| abstract | 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. |
cite
@misc{shujon2026past,
title={PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection},
author={Md. Shakhoyat Rahman Shujon and MD Jahid Hasan Jim and Md. Milon Islam and Md Rezwanul Haque and Fakhri Karray},
year={2026},
eprint={2607.04690},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.04690}
}