@inproceedings{tetakali-tetakali-2026-silkpeak,
title = "{S}ilk{P}eak at {S}em{E}val-2026 Task 6: When Politicians Dodge {---} Unmasking Evasion in Political Interviews through Joint Multi-Task Transformer Learning",
author = "Tetakali, Amruth and
Tetakali, Lavanya",
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-1.253/",
pages = "2020--2025",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes a system for SemEval-2026 Task 6 (CLARITY), which focuses on recognizing evasive communication in political interviews. The approach treats the one subtask{---}determining the clarity level of an answer {---}as a single joint multi-task problem. A DeBERTa-v3-Large encoder is shared across both tasks, processing the question and answer as a single concatenated sequence. By updating independent linear classification heads simultaneously, the model allows the fine-grained learning signals from the evasion taxonomy to directly inform the broader clarity-level decisions, and vice versa. On the official evaluation set, this joint discriminative system achieves a 0.76 macro F1 score on Task 1. This approach significantly outperforms standard single-task baseline models, hierarchical bi-encoding architectures, and generative large language models like LoRA-tuned LLaMA-3-8B."
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<abstract>This paper describes a system for SemEval-2026 Task 6 (CLARITY), which focuses on recognizing evasive communication in political interviews. The approach treats the one subtask—determining the clarity level of an answer —as a single joint multi-task problem. A DeBERTa-v3-Large encoder is shared across both tasks, processing the question and answer as a single concatenated sequence. By updating independent linear classification heads simultaneously, the model allows the fine-grained learning signals from the evasion taxonomy to directly inform the broader clarity-level decisions, and vice versa. On the official evaluation set, this joint discriminative system achieves a 0.76 macro F1 score on Task 1. This approach significantly outperforms standard single-task baseline models, hierarchical bi-encoding architectures, and generative large language models like LoRA-tuned LLaMA-3-8B.</abstract>
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%0 Conference Proceedings
%T SilkPeak at SemEval-2026 Task 6: When Politicians Dodge — Unmasking Evasion in Political Interviews through Joint Multi-Task Transformer Learning
%A Tetakali, Amruth
%A Tetakali, Lavanya
%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 tetakali-tetakali-2026-silkpeak
%X This paper describes a system for SemEval-2026 Task 6 (CLARITY), which focuses on recognizing evasive communication in political interviews. The approach treats the one subtask—determining the clarity level of an answer —as a single joint multi-task problem. A DeBERTa-v3-Large encoder is shared across both tasks, processing the question and answer as a single concatenated sequence. By updating independent linear classification heads simultaneously, the model allows the fine-grained learning signals from the evasion taxonomy to directly inform the broader clarity-level decisions, and vice versa. On the official evaluation set, this joint discriminative system achieves a 0.76 macro F1 score on Task 1. This approach significantly outperforms standard single-task baseline models, hierarchical bi-encoding architectures, and generative large language models like LoRA-tuned LLaMA-3-8B.
%U https://aclanthology.org/2026.semeval-1.253/
%P 2020-2025
Markdown (Informal)
[SilkPeak at SemEval-2026 Task 6: When Politicians Dodge — Unmasking Evasion in Political Interviews through Joint Multi-Task Transformer Learning](https://aclanthology.org/2026.semeval-1.253/) (Tetakali & Tetakali, SemEval 2026)
ACL