---
title: "zhangpeng at SemEval-2026 Task 10: PsyCoMark -"
description: "This SemEval-2026 Task 10 (PsyCoMark) system covers two problems on English Reddit comments. 15 references, cited by 1 paper here."
authors: ["Zhang Peng", "Lu Gehao"]
year: 2026
venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)"
source: "https://aclanthology.org/2026.semeval-1.107/"
doi: "10.18653/v1/2026.semeval-1.107"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/peng-2026-zhangpeng-semeval-2026-task-10/"
---
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# zhangpeng at SemEval-2026 Task 10: PsyCoMark - Psycholinguistic Conspiracy Marker Extraction and Detection

**Authors:** [Zhang Peng](https://md.research.elamur.ai/free/en/authors/zhang-peng/) and [Lu Gehao](https://md.research.elamur.ai/free/en/authors/lu-gehao/) · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.semeval-1.107/) · [PDF](https://aclanthology.org/2026.semeval-1.107.pdf) · [DOI 10.18653/v1/2026.semeval-1.107](https://doi.org/10.18653/v1/2026.semeval-1.107) · [OpenAlex](https://openalex.org/W7166799550)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/peng-2026-zhangpeng-semeval-2026-task-10/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This SemEval-2026 Task 10 (PsyCoMark) system covers two problems on English Reddit comments. The first is locating spans that carry psycholinguistic conspiracy markers (Actor, Action, Effect, Victim, Evidence). The second is deciding whether a comment is conspiratorial. The authors use plain fine-tuning of pre-trained encoders on the official PsyCoMark data only, with no external corpora or augmentation. For marker extraction, character-level annotations are converted to BIO token tags and two multilingual DistilBERT variants are trained with a token-classification head. Predicted tags are then merged back into character spans. For detection, three encoders are fine-tuned separately as binary classifiers, including microsoft/deberta-v3-base. On the test set, span extraction stays weak, with macro-F1 around 0.15. DeBERTa-v3 performs best for detection, reaching a weighted F1 of about 0.73. The work gives a simple, reproducible baseline, with code on GitHub. It also shows how hard fine-grained conspiracy-marker extraction remains compared with document-level detection.

## Key points
- **Subtask 1 test results:** lxyuan/distilbert-base-multilingual-cased-sentiments-student reached macro-F1 0.1476, while distilbert-base-multilingual-cased reached 0.1462. On the dev set the ranking was reversed (0.1762 vs 0.1602).
- **Subtask 2 test results:** microsoft/deberta-v3-base scored weighted F1 0.7267 (accuracy 0.7313), ahead of the two DistilBERT models (0.6800 and 0.6380). Its dev weighted F1 was 0.7582.
- **Detection data:** the training set has 1541 "Yes" and 1990 "No" comments. The 785 "Can't tell" items were dropped. Most texts are under about 100 words, and length does not separate the two classes.
- **Marker counts:** the training markers are imbalanced: Actor 6416, Action 4841, Effect 3739, Evidence 3654, Victim 3315. The authors suggest this imbalance hurts the rarer categories.
- **Training setup:** models were trained for 10 epochs at a learning rate of 2e-5 with weight decay 0.01 and batch size 16 or 32. Proposed future work includes class-balanced learning, augmentation, and cross-lingual transfer.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Yunnan University](https://md.research.elamur.ai/free/en/universities/yunnan-university/) — University, Kunming, China · 17 works · 40 citations

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🧭 Topics (1): [🧒 Psycholinguistics & language acquisition](https://md.research.elamur.ai/free/en/topics/psycholinguistics/) · 102 works

## Cite

Cite as: Zhang Peng and Lu Gehao. 2026. zhangpeng at SemEval-2026 Task 10: PsyCoMark - Psycholinguistic Conspiracy Marker Extraction and Detection. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{peng2026zhangpeng,
  title = {{zhangpeng at SemEval-2026 Task 10: PsyCoMark - Psycholinguistic Conspiracy Marker Extraction and Detection}},
  author = {Zhang Peng and Lu Gehao},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.107},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.107}
}
```

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## References

15 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Use or Not yes no Adnan Akhundov, Dietrich Trautmann, and Georg Groh. 2018. Sequence labeling: A practical approach. arXiv preprint arXiv:1808.03926. — [arXiv:1808.03926](https://arxiv.org/abs/1808.03926) [find](https://scholar.google.com/scholar?q=Use+or+Not+yes+no+Adnan+Akhundov%2C+Dietrich+Trautmann%2C+and+Georg+Groh%2E+2018%2E+Sequence+labeling%3A+A+practical+approach%2E+arXiv+preprint+arXiv%3A1808%2E03926%2E+%E2%80%94+%5BarXiv%3A1808%2E03926%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1808%2E03)
2. Mattia Samory, Felix Soldner, and Veronika Batzdorfer. 2026. [SemEval-2026 task 10: PsyCoMark – psycholinguistic conspiracy marker extraction and detection](https://md.research.elamur.ai/free/en/papers/samory-2026-semeval-2026-task-10-psycomark/). In Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026).
3. Zhi Chen, Jiang Duan, Li Kang, and Guoping Qiu. 2021. Class-imbalanced deep learning via a class-balanced ensemble. IEEE transactions on neural networks and learning systems, 33(10):5626–5640. [find](https://scholar.google.com/scholar?q=Zhi+Chen%2C+Jiang+Duan%2C+Li+Kang%2C+and+Guoping+Qiu%2E+2021%2E+Class%2Dimbalanced+deep+learning+via+a+class%2Dbalanced+ensemble%2E+IEEE+transactions+on+neural+networks+and+learning+systems%2C+33%2810%29%3A5626%E2%80%935640%2E)
4. Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. [find](https://scholar.google.com/scholar?q=Victor+Sanh%2C+Lysandre+Debut%2C+Julien+Chaumond%2C+and+Thomas+Wolf%2E+2019%2E+Distilbert%2C+a+distilled+version+of+bert%3A+smaller%2C+faster%2C+cheaper+and+lighter%2E+arXiv+preprint+arXiv%3A1910%2E01108%2E)
5. Karen M Douglas, Robbie M Sutton, and Aleksandra Cichocka. 2017. The psychology of conspiracy theories. Current directions in psychological science, 26(6):538–542. [find](https://scholar.google.com/scholar?q=Karen+M+Douglas%2C+Robbie+M+Sutton%2C+and+Aleksandra+Cichocka%2E+2017%2E+The+psychology+of+conspiracy+theories%2E+Current+directions+in+psychological+science%2C+26%286%29%3A538%E2%80%93542%2E)
6. Connor Shorten, Taghi M Khoshgoftaar, and Borko Furht. 2021. Text data augmentation for deep learning. Journal of big Data, 8(1):101. [find](https://scholar.google.com/scholar?q=Connor+Shorten%2C+Taghi+M+Khoshgoftaar%2C+and+Borko+Furht%2E+2021%2E+Text+data+augmentation+for+deep+learning%2E+Journal+of+big+Data%2C+8%281%29%3A101%2E)
7. Debanjan Ghosh, Kai North, Ekaterina Kochmar, Mamoru Komachi, and Marcos Zampieri, editors. 2026. Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026). Association for Computational Linguistics, San Diego, United States. [find](https://scholar.google.com/scholar?q=Debanjan+Ghosh%2C+Kai+North%2C+Ekaterina+Kochmar%2C+Mamoru+Komachi%2C+and+Marcos+Zampieri%2C+editors%2E+2026%2E+Proceedings+of+the+20th+International+Workshop+on+Semantic+Evaluation+%28SemEval%2D2026%29%2E+Association+for+)
8. Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip HS Torr. 2015. Conditional random fields as recurrent neural networks. In Proceedings of the IEEE international conference on computer vision, pages 1529–1537. [find](https://scholar.google.com/scholar?q=Shuai+Zheng%2C+Sadeep+Jayasumana%2C+Bernardino+Romera%2DParedes%2C+Vibhav+Vineet%2C+Zhizhong+Su%2C+Dalong+Du%2C+Chang+Huang%2C+and+Philip+HS+Torr%2E+2015%2E+Conditional+random+fields+as+recurrent+neural+networks%2E+In+Proc)
9. Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020. [Deberta: Decoding-enhanced bert with disentangled attention](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/). arXiv preprint arXiv:2006.03654.
10. Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura Barnes, and Donald Brown. 2019. Text classification algorithms: A survey. Information, 10(4):150. [find](https://scholar.google.com/scholar?q=Kamran+Kowsari%2C+Kiana+Jafari+Meimandi%2C+Mojtaba+Heidarysafa%2C+Sanjana+Mendu%2C+Laura+Barnes%2C+and+Donald+Brown%2E+2019%2E+Text+classification+algorithms%3A+A+survey%2E+Information%2C+10%284%29%3A150%2E)
11. Roshan Kumari and Saurabh Kr Srivastava. 2017. Machine learning: A review on binary classification. International Journal of Computer Applications, 160(7). [find](https://scholar.google.com/scholar?q=Roshan+Kumari+and+Saurabh+Kr+Srivastava%2E+2017%2E+Machine+learning%3A+A+review+on+binary+classification%2E+International+Journal+of+Computer+Applications%2C+160%287%29%2E)
12. Guillaume Lample and Alexis Conneau. 2019. Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291. [find](https://scholar.google.com/scholar?q=Guillaume+Lample+and+Alexis+Conneau%2E+2019%2E+Cross%2Dlingual+language+model+pretraining%2E+arXiv+preprint+arXiv%3A1901%2E07291%2E)
13. Lluis Marquez, Pere Comas, Jesús Giménez, and Neus Catala. 2005. Semantic role labeling as sequential tagging. In Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), pages 193–196. [find](https://scholar.google.com/scholar?q=Lluis+Marquez%2C+Pere+Comas%2C+Jes%C3%BAs+Gim%C3%A9nez%2C+and+Neus+Catala%2E+2005%2E+Semantic+role+labeling+as+sequential+tagging%2E+In+Proceedings+of+the+Ninth+Conference+on+Computational+Natural+Language+Learning+%28CoNLL%2D)
14. Behrang Mohit. 2014. Named entity recognition. In Natural language processing of semitic languages, pages 221–245. Springer. [find](https://scholar.google.com/scholar?q=Behrang+Mohit%2E+2014%2E+Named+entity+recognition%2E+In+Natural+language+processing+of+semitic+languages%2C+pages+221%E2%80%93245%2E+Springer%2E)
15. Nam Nguyen and Yunsong Guo. 2007. Comparisons of sequence labeling algorithms and extensions. In Proceedings of the 24th international conference on Machine learning, pages 681–688. [find](https://scholar.google.com/scholar?q=Nam+Nguyen+and+Yunsong+Guo%2E+2007%2E+Comparisons+of+sequence+labeling+algorithms+and+extensions%2E+In+Proceedings+of+the+24th+international+conference+on+Machine+learning%2C+pages+681%E2%80%93688%2E)

*Updated 31 October 2026*