asetclarity at SemEval-2026 Task 6: An Imbalance-Aware RoBERTa Cross-Encoder for Political Response Clarity Classification

AbstractWe address response-clarity classification in political interviews as defined in SemEval-2026 Task 6: CLARITY - Unmasking Political Question Evasions, Task 1, where systems must label each question–answer pair as Clear Reply, Ambivalent, or Clear Non-Reply. We present a reproducible end-to-end pipeline built around a single-stream RoBERTa-large cross-encoder fine-tuned for three-way classification using deterministic text normalization, concatenated QA inputs, and imbalance-aware training (minority oversampling and class-weighted loss). To improve robustness, we train a 5-fold stratified ensemble and combine models via soft-voting. Our official shared-task submission obtains 0.76 macro-F1 on the official leaderboard, ranking 16 out of 41 participating systems. Finally, we deploy the classifier in a lightweight web application supporting both direct text input and audio-based analysis through automatic transcription, enabling interactive inspection of predicted clarity categories.