YNU-HPCC at SemEval-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

The YNU-HPCC team presented their submission to the SemEval-2026 Psycholinguistic Conspiracy Shared Task (Task 10), which involved two distinct challenges: conspiracy marker extraction and conspiracy detection. For the conspiracy marker extraction task, they formulated the problem as a token classification task and fine-tuned pretrained language models, achieving performance above the official baseline and securing a 6th-place ranking on the final leaderboard. In the conspiracy detection task, the team implemented data preprocessing, regularization, and ensemble inference strategies. This methodology resulted in notable improvements over the baseline and earned them a 10th-place ranking. Overall, their findings demonstrate the effectiveness of pretrained language models for both identifying specific conspiracy markers and broader conspiracy content detection.

Key takeaway

For NLP Engineers developing systems to combat online misinformation, particularly conspiracy theories, this work suggests that fine-tuning pretrained language models is a highly effective strategy. You should consider framing marker identification as a token classification problem and integrate data preprocessing, regularization, and ensemble inference for robust conspiracy detection. This approach can significantly improve your system's accuracy and ranking in competitive tasks like SemEval-2026.

Key insights

Pretrained language models effectively address both conspiracy marker extraction and detection tasks.

Principles

• Token classification suits marker extraction.
• Ensemble inference improves detection.
• Data preprocessing enhances model performance.

Method

For marker extraction, fine-tune pretrained language models for token classification. For detection, apply data preprocessing, regularization, and ensemble inference.

In practice

• Employ regularization for robust detection.
• Combine multiple models via ensemble inference.
• Frame marker identification as token classification.

Topics

• SemEval-2026 Task 10
• Conspiracy Detection
• Token Classification
• Pretrained Language Models
• Ensemble Inference
• Psycholinguistics

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection — Data augmentation and self-training can effectively finetune LLMs for specialized text classification tasks with limited data.
• Multi-Stage Training for Abusive Comment Detection in Indic Languages — Multi-stage training with language preprocessing and model ensembles improves abusive comment detection while minimizing false positives.
• An End-to-End Hybrid Framework for Rumour Detection in Low-Resources Algerian Dialect — A hybrid framework combining transformer embeddings with classical classifiers enables effective rumour detection in low-resource Algerian dialect.
• Fake News Detection Using Natural Language Processing! — Combining linguistic, syntactic, and semantic features with machine learning achieves high accuracy in fake news detection.
• Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest — Modern LLMs demonstrate varied capabilities across social media analytics tasks, with GPT-4 excelling in authorship verification and Gemini in attribute inference.
• A Resource for Enthymeme Detection in Controversial Political Discourse — The study of annotator disagreement in enthymeme detection improves model performance and understanding of human inference.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.