Work

KCLarity at SemEval-2026 Task 6: Encoder and Zero-Shot Approaches to Political Evasion Detection

Year: 2026

Type: conference-paper

Abstract: This paper describes the KCLarity team's participation in CLARITY, a shared task at Se-mEval 2026 on classifying ambiguity and evasion techniques in political discourse.We investigate two modelling formulations: (i) directly predicting the clarity label, and (ii) predicting the evasion label and deriving clarity through the task taxonomy hierarchy.We further explore several auxiliary training variants and evaluate decoder-only models in a zeroshot setting under the evasion-first formulation.Overall, the two formulations yield comparable performance.Among encoder-based models, RoBERTa-large achieves the strongest results on the public test set, while zero-shot GPT-5.2 generalises better on the hidden evaluation set. * Equal contribution.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2026.semeval-1.408

Authors: Archie Sage, Salvatore Greco

Institution: King's College London

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: hybrid

Funder: Engineering and Physical Sciences Research Council