@inproceedings{lin-yu-2026-b,
title = "{B} {B} at {S}em{E}val-2026 Task 6: A {R}o{BERT}a-based Model with {NLI}-derived Semantic Features for Clarity-Level Classification of Political Question Evasion",
author = "Lin, Chi-Bo and
Yu, Boyang",
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://aclanthology.org/2026.semeval-1.384/",
pages = "3060--3067",
ISBN = "979-8-89176-414-9",
abstract = "Equivocation and ambiguity are common in political interviews, where public figures often avoid directly answering challenging questions. We present our submission to SemEval-2026 Task 6, Subtask 1 on English political response clarity classification. Our system builds on RoBERTa and incorporates NLI-derived semantic features to distinguish Clear Reply, Ambivalent, and Clear Non-Reply responses. To address class imbalance and performance instability, we explore class weighting, multi-seed ensembling, and a hierarchical two-stage framework with threshold tuning. Our best model achieves 60{\%} macro-F1 on the official test set and 64{\%} macro-F1 on an additional evaluation set, demonstrating stable performance across splits. Our results show that carefully engineered smaller models, combined with structured semantic features and imbalance-aware training, provide an effective and computationally efficient solution under limited training data."
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<abstract>Equivocation and ambiguity are common in political interviews, where public figures often avoid directly answering challenging questions. We present our submission to SemEval-2026 Task 6, Subtask 1 on English political response clarity classification. Our system builds on RoBERTa and incorporates NLI-derived semantic features to distinguish Clear Reply, Ambivalent, and Clear Non-Reply responses. To address class imbalance and performance instability, we explore class weighting, multi-seed ensembling, and a hierarchical two-stage framework with threshold tuning. Our best model achieves 60% macro-F1 on the official test set and 64% macro-F1 on an additional evaluation set, demonstrating stable performance across splits. Our results show that carefully engineered smaller models, combined with structured semantic features and imbalance-aware training, provide an effective and computationally efficient solution under limited training data.</abstract>
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%0 Conference Proceedings
%T B B at SemEval-2026 Task 6: A RoBERTa-based Model with NLI-derived Semantic Features for Clarity-Level Classification of Political Question Evasion
%A Lin, Chi-Bo
%A Yu, Boyang
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F lin-yu-2026-b
%X Equivocation and ambiguity are common in political interviews, where public figures often avoid directly answering challenging questions. We present our submission to SemEval-2026 Task 6, Subtask 1 on English political response clarity classification. Our system builds on RoBERTa and incorporates NLI-derived semantic features to distinguish Clear Reply, Ambivalent, and Clear Non-Reply responses. To address class imbalance and performance instability, we explore class weighting, multi-seed ensembling, and a hierarchical two-stage framework with threshold tuning. Our best model achieves 60% macro-F1 on the official test set and 64% macro-F1 on an additional evaluation set, demonstrating stable performance across splits. Our results show that carefully engineered smaller models, combined with structured semantic features and imbalance-aware training, provide an effective and computationally efficient solution under limited training data.
%U https://aclanthology.org/2026.semeval-1.384/
%P 3060-3067
Markdown (Informal)
[B B at SemEval-2026 Task 6: A RoBERTa-based Model with NLI-derived Semantic Features for Clarity-Level Classification of Political Question Evasion](https://aclanthology.org/2026.semeval-1.384/) (Lin & Yu, SemEval 2026)
ACL