---
title: "Stance Detection Benchmark — paper by Schiller et al. (2020)"
description: "The authors therefore build a benchmark of ten StD datasets drawn from five domains. 49 references, cited by 43 papers here."
authors: ["Benjamin Schiller", "Johannes Daxenberger", "Iryna Gurevych"]
year: 2020
venue: "arXiv:2001.01565"
source: "https://arxiv.org/abs/2001.01565"
doi: "10.48550/arxiv.2001.01565"
arxiv: "2001.01565"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/schiller-2020-stance-detection-benchmark-robust-stance/"
---
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# Stance Detection Benchmark: How Robust Is Your Stance Detection?

**Authors:** [Benjamin Schiller](https://md.research.elamur.ai/free/en/authors/benjamin-schiller/), [Johannes Daxenberger](https://md.research.elamur.ai/free/en/authors/johannes-daxenberger/) and [Iryna Gurevych](https://md.research.elamur.ai/free/en/authors/iryna-gurevych/) · **Year:** 2020 · **Venue:** arXiv:2001.01565 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2001.01565) · [PDF](https://arxiv.org/pdf/2001.01565) · [DOI 10.48550/arxiv.2001.01565](https://doi.org/10.48550/arxiv.2001.01565) · [arXiv 2001.01565](https://arxiv.org/abs/2001.01565) · [OpenAlex](https://openalex.org/W2998594465)

**Citations:** 81 citations (OpenAlex, 2026-10-14) · [43](https://md.research.elamur.ai/free/en/papers/schiller-2020-stance-detection-benchmark-robust-stance/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

Stance Detection (StD) means deciding whether a piece of text, such as a tweet, post, claim or news article, supports or opposes a given topic or claim. It serves as a foundation for tasks like spotting fake news, verifying claims and retrieving arguments. Humans agree on stance far more often than models predict it correctly. StD datasets also differ widely in domain, input format, label set and size. The authors therefore build a benchmark of ten StD datasets drawn from five domains. They train them jointly in a multi-dataset learning (MDL) setup and add transfer learning (TL) from related tasks, adapting the MT-DNN framework. Both techniques raise accuracy on average, and the combined model sets new state-of-the-art results on five datasets. However, simple adversarial attacks (spelling errors, negation, paraphrasing) hurt the MDL model more than single-dataset (SDL) BERT models. Low-resource experiments suggest the cause is overfitting to dataset biases when training data is abundant. The work argues that multi-task NLP needs de-biasing methods and releases the full benchmark for reuse.

## Key points

- The benchmark covers ten corpora, including arc, argmin, fnc1, iac1, ibmcs, perspectrum, scd, semeval2016t6, semeval2019t7 and snopes. Their training sizes range from 2,394 to 75,385 samples.
- Average gains are about +4 pp from MDL and +3.4 pp from TL. The gap to human agreement remains large, for example FNC-1 at 61.1% versus 75.4% inter-annotator agreement.
- Robustness is measured with Resilience_rel, a modified version of the Resilience score of Thorne et al. (2019) that also accounts for clean test-set performance. With full training data, the overall score is 92.9% for MT-DNN MDL versus 96.7% for BERT SDL.
- Shrinking the training data to 10% narrows this robustness gap from 3.8 pp to 1.5 pp, mainly on the negation and paraphrase attacks. At the same time, MDL's test-score advantage over SDL grows from .0514 to .0953.
- Data splits, models and code are available at github.com/UKPLab/mdl-stance-robustness.

*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

- [Technische Universität Darmstadt](https://md.research.elamur.ai/free/en/universities/technische-universitat-darmstadt/) — University, Darmstadt, Germany · 114 works · 36,326 citations

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## Cite

Cite as: Benjamin Schiller, Johannes Daxenberger and Iryna Gurevych. 2020. Stance Detection Benchmark: How Robust Is Your Stance Detection?. arXiv:2001.01565.

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

```bibtex
@misc{schiller2020stance,
  title = {{Stance Detection Benchmark: How Robust Is Your Stance Detection?}},
  author = {Benjamin Schiller, Johannes Daxenberger and Iryna Gurevych},
  year = {2020},
  doi = {10.48550/arxiv.2001.01565},
  eprint = {2001.01565},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2001.01565}
}
```

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## References

49 references, 27 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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48. Ruoyao Yang, Wanying Xie, Chunhua Liu, and Dong Yu. 2019. BLCU NLP at SemEval-2019 task 7: An inference chain-based GPT model for rumour evaluation. In SemEval-2019, pages 1090–1096. [find](https://scholar.google.com/scholar?q=Ruoyao+Yang%2C+Wanying+Xie%2C+Chunhua+Liu%2C+and+Dong+Yu%2E+2019%2E+BLCU+NLP+at+SemEval%2D2019+task+7%3A+An+inference+chain%2Dbased+GPT+model+for+rumour+evaluation%2E+In+SemEval%2D2019%2C+pages+1090%E2%80%931096%2E)
49. Qiang Zhang, Emine Yilmaz, and Shangsong Liang. 2018. Ranking-based method for news stance detection. In WWW’18, pages 41–42. [find](https://scholar.google.com/scholar?q=Qiang+Zhang%2C+Emine+Yilmaz%2C+and+Shangsong+Liang%2E+2018%2E+Ranking%2Dbased+method+for+news+stance+detection%2E+In+WWW%E2%80%9918%2C+pages+41%E2%80%9342%2E)

*Updated 2 November 2026*