KvochurHegel at StanceNakba: Robust Stance Detection with Regularized Natural Language Inference

Abstract

Actor-level stance detection over noisy, politically sensitive data can present challenges that standard training procedures fail to handle reliably. This paper presents KvochurHegel, our submission to the StanceNakba 2026 Shared Task, which addresses these challenges by framing stance classification as Natural Language Inference (NLI) to capture actor-level granularity. The official StanceNakba dataset contains high label noise and topic-correlated spurious features, such as texts discussing unrelated global conflicts using in-domain political vocabulary. To handle these conditions within a three-class schema, we construct templates encoding stance hypotheses for specific actors (e.g., "The author expresses support for Palestine") and introduce a broadened neutral class designed to absorb spurious out-of-domain inputs. A DeBERTa-v3 Cross-Encoder independently evaluates the entailment between the input text and each class-specific hypothesis. Because standard cross-entropy training tends to memorize contradictory annotations under these conditions, we regularize the training procedure with R-Drop and label smoothing. This regularized setup likely contributed to robustness against distribution shifts between the competition’s evaluation phases (the public leaderboard and private test set), allowing our model to improve from a Macro-F1 of 0.9094 to 0.9384 without requiring large generative models, cross-validation, or inference-time ensembling.

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