asetclarity at SemEval-2026 Task 6: An Imbalance-Aware RoBERTa Cross-Encoder for Political Response Clarity Classification
Year: 2026
Type: conference-paper
Abstract: We 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.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166816940