This paper presents the Duluth approach toSemEval-2026 Task 6 on CLARITY: Unmask-ing Political Question Evasions. We addressTask 1 (clarity-level classification) and Task 2(evasion-level classification), both of which in-volve classifying question–answer pairs fromU.S. presidential interviews using a two-leveltaxonomy of response clarity. Our system isbased on DeBERTa-V3-base, extended withfocal loss, layer-wise learning rate decay, andboolean discourse features. To address classimbalance in the training data, we augmentminority classes using synthetic examples gen-erated by Gemini 3 and Claude Sonnet 4.5. Ourbest configuration achieved a Macro F1 of 0.76on the Task 1 evaluation set, placing 8th outof 40 teams. The top-ranked system (TeleAI)achieved 0.89, while the mean score across par-ticipants was 0.70. Error analysis reveals thatthe dominant source of misclassification is con-fusion between Ambivalent and Clear Replyresponses, a pattern that mirrors disagreementsamong human annotators. Our findings demon-strate that LLM-based data augmentation canmeaningfully improve minority-class recall onnuanced political discourse tasks.

This paper presents our approach to the SemEval-2025 Task 7 on Multilingual and Crosslingual Fact-Checked Claim Retrieval. We implemented a TF-IDF-based retrieval system with experimentation on vector dimensions and tokenization strategies. Our best-performing configuration used word-level tokenization with a vocabulary size of 15,000 features, achieving an average success@10 score of 0.78 on the development set and 0.69 on the test set across ten languages. Our system showed stronger performance on higher resource languages with large performance gaps compared to the top-ranked system, which achieved 0.96 average success@10. Our findings suggest that though advanced neural architectures are increasingly dominant in multilingual retrieval tasks, properly optimized traditional methods like TF-IDF remain competitive baselines, especially in limited resource scenarios.