@inproceedings{marhoefer-etal-2026-ucsc,
title = "{UCSC} {NLP} at {S}em{E}val-2026 Task 10: Boundary-Aware Span Extraction and {R}o{BERT}a Classification for Conspiracy Detection",
author = "Marhoefer, Dom and
Suvakovic, Milos and
Grant-Richards, Glenn and
Pinero, Aidan and
King, Ryan",
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-1.194/",
pages = "1495--1500",
ISBN = "979-8-89176-414-9",
abstract = "We present our systems for SemEval-2026 Task10 (PsyCoMark), addressing conspiracy markerextraction (Subtask 1) and document-level con-spiracy detection (Subtask 2). For marker ex-traction, we formulate the task as multi-labelspan classification over enumerated candidatespans, using IoU{\ensuremath{\geq}}0.95 positive labeling, hard-negative sampling, and containment-based non-maximum suppression (NMS) with boundary-aware span representations. Document classi-fication is modeled independently using a se-quence classifier with label smoothing and astratified train{--}validation split. Analysis showsthat entity-like roles (Actor, Victim) are de-tected robustly, while abstract roles (Action,Effect, Evidence) remain sensitive to boundarycriteria. On the official test set, our systemsrank 7th in Subtask 1 (0.2251 macro F1) and12th in Subtask 2 (0.7694 weighted F1)."
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<title>UCSC NLP at SemEval-2026 Task 10: Boundary-Aware Span Extraction and RoBERTa Classification for Conspiracy Detection</title>
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<abstract>We present our systems for SemEval-2026 Task10 (PsyCoMark), addressing conspiracy markerextraction (Subtask 1) and document-level con-spiracy detection (Subtask 2). For marker ex-traction, we formulate the task as multi-labelspan classification over enumerated candidatespans, using IoU\ensuremath\geq0.95 positive labeling, hard-negative sampling, and containment-based non-maximum suppression (NMS) with boundary-aware span representations. Document classi-fication is modeled independently using a se-quence classifier with label smoothing and astratified train–validation split. Analysis showsthat entity-like roles (Actor, Victim) are de-tected robustly, while abstract roles (Action,Effect, Evidence) remain sensitive to boundarycriteria. On the official test set, our systemsrank 7th in Subtask 1 (0.2251 macro F1) and12th in Subtask 2 (0.7694 weighted F1).</abstract>
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%0 Conference Proceedings
%T UCSC NLP at SemEval-2026 Task 10: Boundary-Aware Span Extraction and RoBERTa Classification for Conspiracy Detection
%A Marhoefer, Dom
%A Suvakovic, Milos
%A Grant-Richards, Glenn
%A Pinero, Aidan
%A King, Ryan
%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 marhoefer-etal-2026-ucsc
%X We present our systems for SemEval-2026 Task10 (PsyCoMark), addressing conspiracy markerextraction (Subtask 1) and document-level con-spiracy detection (Subtask 2). For marker ex-traction, we formulate the task as multi-labelspan classification over enumerated candidatespans, using IoU\ensuremath\geq0.95 positive labeling, hard-negative sampling, and containment-based non-maximum suppression (NMS) with boundary-aware span representations. Document classi-fication is modeled independently using a se-quence classifier with label smoothing and astratified train–validation split. Analysis showsthat entity-like roles (Actor, Victim) are de-tected robustly, while abstract roles (Action,Effect, Evidence) remain sensitive to boundarycriteria. On the official test set, our systemsrank 7th in Subtask 1 (0.2251 macro F1) and12th in Subtask 2 (0.7694 weighted F1).
%U https://aclanthology.org/2026.semeval-1.194/
%P 1495-1500
Markdown (Informal)
[UCSC NLP at SemEval-2026 Task 10: Boundary-Aware Span Extraction and RoBERTa Classification for Conspiracy Detection](https://aclanthology.org/2026.semeval-1.194/) (Marhoefer et al., SemEval 2026)
ACL