ThinkVision at SemEval-2026 Task 6: A Transformer-Based Ensemble System for Clarity Detection
Year: 2026
Type: conference-paper
Abstract: We study the problem of assessing the clarity of political question-answer pairs, where the goal is to determine whether a response directly addresses the question, avoids it, or remains ambiguous.This task is particularly challenging in political discourse, where evasiveness can be subtle and context-dependent.To tackle this problem, we propose an ensemblebased approach built on the transformer-based model DeBERTa-v3-base, fine-tuned on concatenated question-answer inputs.Special attention is given to class imbalance during training to ensure robust performance across all categories.To better capture uncertainty in borderline cases, we train multiple models with different random seeds and employ Monte Carlo Dropout at inference time.Final predictions are obtained by averaging logits across ensemble models and stochastic forward passes, yielding a more stable decision boundary.Our system achieves a Macro-F1 score of 0.76 on the evaluation dataset.Error analysis reveals that responses that partially engage with the topic while failing to provide a direct answer remain the most challenging, highlighting the inherent difficulty of detecting nuanced evasiveness in political communication.
Institution: Indian Institute of Information Technology Dharwad
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166799950