SyntaxMind at SemEval-2026 Task 6: Exploring Transformers and LLMs for Unmasking Political Question Evasions
Year: 2026
Type: conference-paper
Abstract: This paper describes our approach to Subtask 1: Clarity-level Classification in SemEval-2026 Task 6.The task focuses on determining the clarity of political responses with respect to their corresponding questions.To enhance model performance, we introduced a direct answer generation strategy as an additional input feature and applied Task-Adaptive Pre-Training (TAPT) to enhance encoder-only Transformer models with the task domain.We further explored both cross-entropy and focal loss to address potential class imbalance.Experimental results show that TAPT enhanced encoder models, particularly DeBERTa-V3-base, achieved the strongest performance, while generative small language models fine-tuned via parameter efficient methods exhibited comparatively lower results.Our system obtained a macro-F1 score of 0.72 on the official evaluation set, ranking 24th out of 40 teams.
Author: Md. Shihab Uddin Riad
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166800197