@inproceedings{marchitan-dinu-2026-unibuc,
title = "{U}nibuc-{NLP} at {S}em{E}val-2026 Task 10: Unmasking Conspiracies with Pre-Trained Language Models",
author = "Marchitan, Teodor-George and
Dinu, Liviu",
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-1.178/",
pages = "1379--1384",
ISBN = "979-8-89176-414-9",
abstract = "The paper describes the system submitted to SemEval-2026 Task 10 (PsyCoMark) Subtask 2: detecting whether a Reddit comment expresses a conspiracy belief. We investigate three modeling paradigms: (A) an embedding-and-classify pipeline using Jina-embeddings-v3, HateBERT and BERT-Sentiment with Optuna-tuned classical ML models, optionally enriched by 19 readability features from textstat; (B) end-to-end fine-tuning of encoder transformers (DeBERTa-v3-base, DistilBERT) with a compact 128-unit classifier head and multiple pooling strategies; and (C) parameter-efficient QLoRA fine-tuning of large decoder-only models (Mistral-7B-v0.3, Qwen3-0.6B). Our best system, DeBERTa-v3-base with a 128-dimensional classifier, achieves a weighted F1 of 0.74, ranking 29/52 on the official leaderboard. Post-submission analysis further reveals that a weighted pooling strategy outperforms [CLS] on the official validation split by +0.04, achieving a weighted F1 of 0.78 (rank 8/52), suggesting that conspiracy-relevant features are distributed across transformer layers rather than concentrated at the final output."
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<abstract>The paper describes the system submitted to SemEval-2026 Task 10 (PsyCoMark) Subtask 2: detecting whether a Reddit comment expresses a conspiracy belief. We investigate three modeling paradigms: (A) an embedding-and-classify pipeline using Jina-embeddings-v3, HateBERT and BERT-Sentiment with Optuna-tuned classical ML models, optionally enriched by 19 readability features from textstat; (B) end-to-end fine-tuning of encoder transformers (DeBERTa-v3-base, DistilBERT) with a compact 128-unit classifier head and multiple pooling strategies; and (C) parameter-efficient QLoRA fine-tuning of large decoder-only models (Mistral-7B-v0.3, Qwen3-0.6B). Our best system, DeBERTa-v3-base with a 128-dimensional classifier, achieves a weighted F1 of 0.74, ranking 29/52 on the official leaderboard. Post-submission analysis further reveals that a weighted pooling strategy outperforms [CLS] on the official validation split by +0.04, achieving a weighted F1 of 0.78 (rank 8/52), suggesting that conspiracy-relevant features are distributed across transformer layers rather than concentrated at the final output.</abstract>
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%0 Conference Proceedings
%T Unibuc-NLP at SemEval-2026 Task 10: Unmasking Conspiracies with Pre-Trained Language Models
%A Marchitan, Teodor-George
%A Dinu, Liviu
%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 marchitan-dinu-2026-unibuc
%X The paper describes the system submitted to SemEval-2026 Task 10 (PsyCoMark) Subtask 2: detecting whether a Reddit comment expresses a conspiracy belief. We investigate three modeling paradigms: (A) an embedding-and-classify pipeline using Jina-embeddings-v3, HateBERT and BERT-Sentiment with Optuna-tuned classical ML models, optionally enriched by 19 readability features from textstat; (B) end-to-end fine-tuning of encoder transformers (DeBERTa-v3-base, DistilBERT) with a compact 128-unit classifier head and multiple pooling strategies; and (C) parameter-efficient QLoRA fine-tuning of large decoder-only models (Mistral-7B-v0.3, Qwen3-0.6B). Our best system, DeBERTa-v3-base with a 128-dimensional classifier, achieves a weighted F1 of 0.74, ranking 29/52 on the official leaderboard. Post-submission analysis further reveals that a weighted pooling strategy outperforms [CLS] on the official validation split by +0.04, achieving a weighted F1 of 0.78 (rank 8/52), suggesting that conspiracy-relevant features are distributed across transformer layers rather than concentrated at the final output.
%U https://aclanthology.org/2026.semeval-1.178/
%P 1379-1384
Markdown (Informal)
[Unibuc-NLP at SemEval-2026 Task 10: Unmasking Conspiracies with Pre-Trained Language Models](https://aclanthology.org/2026.semeval-1.178/) (Marchitan & Dinu, SemEval 2026)
ACL