@inproceedings{akram-fatima-2026-nust,
title = "{NUST} {P}sy{AI} at {S}em{E}val-2026 Task 10: Parameter-Efficient {R}o{BERT}a for Conspiracy Detection and Character-Level Marker Extraction",
author = "Akram, Mian Muhammad Husnain and
Fatima, Mehwish",
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-1.170/",
pages = "1298--1307",
ISBN = "979-8-89176-414-9",
abstract = "We present the NUST PsyAI system for SemEval-2026 Task 10 (PsyCoMark), targeting document-level conspiracy detection and character-level psycholinguistic marker extraction from Reddit discourse. Our system ranks 7th in Extraction and 8th in Detection on the leaderboard. We benchmark feature-based and transformer approaches, adopting RoBERTalarge with LoRA for parameter-efficient finetuning. For detection, RB-DET-LoRA outperforms all baselines, achieving weighted F1 0.79 (dev) and 0.76 (test), with robust generalization under blinded evaluation. For extraction, we contrast a unified multi-type BIO scheme with a decomposed per-type setup; the latter mitigates cross-label interference and improves boundary consistency, reaching Overlap F1 of 0.16 (dev) and 0.21 (test). Results reveal a clear asymmetry: detection benefits from contextual semantic modeling, while extraction is limited by sparse supervision and boundary-sensitive evaluation."
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<abstract>We present the NUST PsyAI system for SemEval-2026 Task 10 (PsyCoMark), targeting document-level conspiracy detection and character-level psycholinguistic marker extraction from Reddit discourse. Our system ranks 7th in Extraction and 8th in Detection on the leaderboard. We benchmark feature-based and transformer approaches, adopting RoBERTalarge with LoRA for parameter-efficient finetuning. For detection, RB-DET-LoRA outperforms all baselines, achieving weighted F1 0.79 (dev) and 0.76 (test), with robust generalization under blinded evaluation. For extraction, we contrast a unified multi-type BIO scheme with a decomposed per-type setup; the latter mitigates cross-label interference and improves boundary consistency, reaching Overlap F1 of 0.16 (dev) and 0.21 (test). Results reveal a clear asymmetry: detection benefits from contextual semantic modeling, while extraction is limited by sparse supervision and boundary-sensitive evaluation.</abstract>
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%0 Conference Proceedings
%T NUST PsyAI at SemEval-2026 Task 10: Parameter-Efficient RoBERTa for Conspiracy Detection and Character-Level Marker Extraction
%A Akram, Mian Muhammad Husnain
%A Fatima, Mehwish
%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 akram-fatima-2026-nust
%X We present the NUST PsyAI system for SemEval-2026 Task 10 (PsyCoMark), targeting document-level conspiracy detection and character-level psycholinguistic marker extraction from Reddit discourse. Our system ranks 7th in Extraction and 8th in Detection on the leaderboard. We benchmark feature-based and transformer approaches, adopting RoBERTalarge with LoRA for parameter-efficient finetuning. For detection, RB-DET-LoRA outperforms all baselines, achieving weighted F1 0.79 (dev) and 0.76 (test), with robust generalization under blinded evaluation. For extraction, we contrast a unified multi-type BIO scheme with a decomposed per-type setup; the latter mitigates cross-label interference and improves boundary consistency, reaching Overlap F1 of 0.16 (dev) and 0.21 (test). Results reveal a clear asymmetry: detection benefits from contextual semantic modeling, while extraction is limited by sparse supervision and boundary-sensitive evaluation.
%U https://aclanthology.org/2026.semeval-1.170/
%P 1298-1307
Markdown (Informal)
[NUST PsyAI at SemEval-2026 Task 10: Parameter-Efficient RoBERTa for Conspiracy Detection and Character-Level Marker Extraction](https://aclanthology.org/2026.semeval-1.170/) (Akram & Fatima, SemEval 2026)
ACL