PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection
Summary
PAST-TIDE is a stance detection system for the StanceNakba Shared Task, using statement tuning with cloze-style masked language modeling, prototypical contrastive learning, and topic-conditional layer normalization for cross-topic Arabic stance detection, achieving macro-F1 scores of 0.75 and 0.74 on subtasks A and B.
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Paper page - PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection
Source: https://huggingface.co/papers/2607.04690
Abstract
WeintroducePAST-TIDE,ourstancedetectionsystemaddressingbothsubtasksoftheStanceNakbaSharedTaskatNakbaNLP@LREC-COLING2026.Themainideaisstatementtuning.Weredefinestanceascloze-stylemaskedlanguagemodeling(MLM),lettingaverbalizermaplabelwordstostancecategoriesthroughthepre-trainedMLMheadratherthanappendingarandomlyinitializedclassificationhead.Wecomplementthiswithprototypicalcontrastivelearning,whichuseslearnableclassprototypesforbatch-sizeindependentcontrastivetraining,andtopic-conditionallayernormalizationforcross-topicArabicstancedetection.PAST-TIDEachievesmacro-F1scoresof0.75forSubtaskAand0.74forSubtaskBontheofficialleaderboard,indicatingthatminimalarchitecturaladditionstoapre-trainedmodelcanremaincompetitiveinlow-resourcesettings.
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