@inproceedings{turk-etal-2026-clac,
title = "{CL}a{C} at {S}em{E}val-2026 Task 6: Response Clarity Detection in Political Discourse",
author = "Turk, Nawar and
Miquet-Westphal, Lucas and
Kosseim, Leila",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.238/",
pages = "1890--1902",
ISBN = "979-8-89176-414-9",
abstract = "In this paper, we present our system for SemEval-2026 Task 6 (CLARITY) on response clarity and evasion detection in question-answer pairs from U.S. presidential interviews, comparing fine-tuned encoders with prompt-based LLMs. Our LLM ensemble achieves 80 macro-F1 on the 3-class Task 1 (9th/41) and 59 on the 9-class Task 2 (3rd/33). Across 8 transformer encoders optimized through a four-stage pipeline, partial encoder layer unfreezing outperforms full fine-tuning by a wide margin. Combining English and multilingual encoders further improves ensemble performance over either family alone, despite multilingual models being individually weaker. Prompt-based LLMs, without any task-specific parameter updates, outperform fine-tuned encoders, particularly on minority classes; among open-weight LLMs, parameter count does not predict performance. Enriched input, concatenating the full interviewer turn, improves LLM performance but not that of encoders, an effect that persists with Longformer{'}s extended context window, suggesting the divergence is not attributable to sequence-length capacity alone in our settings. The Clear Reply/Ambivalent boundary remains the dominant failure mode, mirroring the disagreement among human annotators. Our code, prompts, model configurations, and results are publicly available."
}Markdown (Informal)
[CLaC at SemEval-2026 Task 6: Response Clarity Detection in Political Discourse](https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.238/) (Turk et al., SemEval 2026)
ACL