---
title: "Distilling Calibrated Knowledge for Stance — Li 2023"
description: "The paper addresses stance detection, which means deciding whether an author is for or against a given target, such as marijuana legalization."
authors: ["Yingjie Li", "Cornelia Caragea"]
year: 2023
venue: "Findings of the Association for Computational Linguistics: ACL 2023"
source: "https://aclanthology.org/2023.findings-acl.393/"
doi: "10.18653/v1/2023.findings-acl.393"
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/papers/li-2023-distilling-calibrated-knowledge-stance-detection/"
---
[Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/li-2023-distilling-calibrated-knowledge-stance-detection/)

[home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2023](https://md.research.elamur.ai/free/papers/2023/page/2/) / Distilling Calibrated Knowledge for Stance Detection

# Distilling Calibrated Knowledge for Stance Detection

**Authors:** [Yingjie Li](https://md.research.elamur.ai/free/authors/yingjie-li/), [Cornelia Caragea](https://md.research.elamur.ai/free/authors/cornelia-caragea/) · **Year:** 2023 · **Venue:** Findings of the Association for Computational Linguistics: ACL 2023 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.findings-acl.393/) · [PDF](https://aclanthology.org/2023.findings-acl.393.pdf) · [DOI 10.18653/v1/2023.findings-acl.393](https://doi.org/10.18653/v1/2023.findings-acl.393) · [OpenAlex](https://openalex.org/W4385572431)

**Citations:** 5 citations (openalex-snapshot · 2026-10-14) · [8](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses stance detection, which means deciding whether an author is for or against a given target, such as marijuana legalization. Most existing systems learn only from one-hot labels. That discards useful information about how the stance classes relate to each other. The authors study knowledge distillation (KD) for this task. They train born-again networks over several generations, where each student becomes the teacher for a new student with the same architecture. They found that teachers in an earlier adaptive method (AKD) are poorly calibrated and give overconfident, sharply peaked outputs. To fix this, they propose CKD (Calibration-based Knowledge Distillation). CKD applies temperature scaling to the teacher's predictions and updates the temperature dynamically at every generation. The student then learns from both hard labels and these calibrated soft labels. In multi-target training experiments on three datasets (COVID-19, AM and P-Stance), CKD consistently beats strong baselines and yields the best-calibrated teachers. The work matters because it links teacher calibration to distillation quality: smoother teacher signals help students learn inter-class similarity.

## Key points
- Datasets: COVID-19 (Glandt et al., 2021), AM (Stab et al., 2018) and P-Stance (Li et al., 2021a). All use a multi-target setup, with one model trained on all targets of a dataset.
- Training over multiple generations improves results for both CKD and an extended multi-generation AKD. CKD outperforms AKD in most generations.
- CKD's teachers have the lowest Expected Calibration Error (ECE): 2.48% on COVID-19, 3.79% on AM and 3.58% on P-Stance. AKD's teachers score 12.38%, 17.70% and 7.73% respectively. Reliability diagrams confirm that AKD is overconfident.
- The best CKD student reaches the highest micro-averaged F1 on all three datasets. A calibration bin size of 10 works best overall.
- Limitations: multiple generations raise training time and memory use, and the gains level off after a few generations. Code is released at github.com/chuchun8/CKD.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

8 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 7 not yet.

- [Multi-modal Stance Detection: New Datasets and Model](https://md.research.elamur.ai/free/papers/liang-2024-multi-modal-stance-detection-new/)

## Cite

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

```bibtex
@inproceedings{li2023distilling,
  title = {{Distilling Calibrated Knowledge for Stance Detection}},
  author = {Li, Yingjie and Caragea, Cornelia},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
  year = {2023},
  doi = {10.18653/v1/2023.findings-acl.393},
  url = {https://doi.org/10.18653/v1/2023.findings-acl.393}
}
```

- [← previous Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion](https://md.research.elamur.ai/free/papers/ko-2023-discourse-analysis-questions-answers-parsing/)
- [next → Distilling Reasoning Capabilities into Smaller Language Models](https://md.research.elamur.ai/free/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/papers/2023/page/2/)

## References

56 references, 21 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Gustavo Aguilar, Yuan Ling, Yu Zhang, Benjamin Yao, Xing Fan, and Chenlei Guo. 2020. Knowledge distillation from internal representations. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, pages 7350–7357. [find](https://scholar.google.com/scholar?q=Gustavo+Aguilar%2C+Yuan+Ling%2C+Yu+Zhang%2C+Benjamin+Yao%2C+Xing+Fan%2C+and+Chenlei+Guo%2E+2020%2E+Knowledge+distillation+from+internal+representations%2E+In+The+Thirty%2DFourth+AAAI+Conference+on+Artificial+Intelligen)
2. Abeer AlDayel and Walid Magdy. 2020. Stance detection on social media: State of the art and trends. arXiv preprint arXiv:2006.03644. [find](https://scholar.google.com/scholar?q=Abeer+AlDayel+and+Walid+Magdy%2E+2020%2E+Stance+detection+on+social+media%3A+State+of+the+art+and+trends%2E+arXiv+preprint+arXiv%3A2006%2E03644%2E)
3. Emily Allaway and Kathleen McKeown. 2020. Zero-shot stance detection: A dataset and model using generalized topic representations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8913– 8931. [find](https://scholar.google.com/scholar?q=Emily+Allaway+and+Kathleen+McKeown%2E+2020%2E+Zero%2Dshot+stance+detection%3A+A+dataset+and+model+using+generalized+topic+representations%2E+In+Proceedings+of+the+2020+Conference+on+Empirical+Methods+in+Natural) [our card](https://md.research.elamur.ai/free/papers/allaway-2020-zero-shot-stance-detection-dataset/)
4. Emily Allaway, Malavika Srikanth, and Kathleen McKeown. 2021. Adversarial learning for zero-shot stance detection on social media. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4756–4767. [find](https://scholar.google.com/scholar?q=Emily+Allaway%2C+Malavika+Srikanth%2C+and+Kathleen+McKeown%2E+2021%2E+Adversarial+learning+for+zero%2Dshot+stance+detection+on+social+media%2E+In+Proceedings+of+the+2021+Conference+of+the+North+American+Chapter+o) [our card](https://md.research.elamur.ai/free/papers/allaway-2021-adversarial-learning-zero-shot-stance/)
5. Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, and Kalina Bontcheva. 2016. Stance detection with bidirectional conditional encoding. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 876–885. [find](https://scholar.google.com/scholar?q=Isabelle+Augenstein%2C+Tim+Rockt%C3%A4schel%2C+Andreas+Vlachos%2C+and+Kalina+Bontcheva%2E+2016%2E+Stance+detection+with+bidirectional+conditional+encoding%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Methods+) [our card](https://md.research.elamur.ai/free/papers/augenstein-2016-stance-detection-bidirectional-conditional-encoding/)
6. Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, and Quoc V. Le. 2019. Bam! Born-again multi-task networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5931–5937. [find](https://scholar.google.com/scholar?q=Kevin+Clark%2C+Minh%2DThang+Luong%2C+Urvashi+Khandelwal%2C+Christopher+D%2E+Manning%2C+and+Quoc+V%2E+Le%2E+2019%2E+Bam%21+Born%2Dagain+multi%2Dtask+networks+for+natural+language+understanding%2E+In+Proceedings+of+the+57th+Annu)
7. Anna Currey, Prashant Mathur, and Georgiana Dinu. 2020. Distilling multiple domains for neural machine translation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4500–4511. [find](https://scholar.google.com/scholar?q=Anna+Currey%2C+Prashant+Mathur%2C+and+Georgiana+Dinu%2E+2020%2E+Distilling+multiple+domains+for+neural+machine+translation%2E+In+Proceedings+of+the+2020+Conference+on+Empirical+Methods+in+Natural+Language+Proce)
8. Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Arkaitz Zubiaga. 2017. SemEval-2017 task 8: RumourEval: Determining rumour veracity and support for rumours. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 69–76… [find](https://scholar.google.com/scholar?q=Leon+Derczynski%2C+Kalina+Bontcheva%2C+Maria+Liakata%2C+Rob+Procter%2C+Geraldine+Wong+Sak+Hoi%2C+and+Arkaitz+Zubiaga%2E+2017%2E+SemEval%2D2017+task+8%3A+RumourEval%3A+Determining+rumour+veracity+and+support+for+rumours%2E+)
9. Shrey Desai and Greg Durrett. 2020. Calibration of pre-trained transformers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 295–302. [find](https://scholar.google.com/scholar?q=Shrey+Desai+and+Greg+Durrett%2E+2020%2E+Calibration+of+pre%2Dtrained+transformers%2E+In+Proceedings+of+the+2020+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2C+pages+295%E2%80%93302%2E)
10. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,… [find](https://scholar.google.com/scholar?q=Jacob+Devlin%2C+Ming%2DWei+Chang%2C+Kenton+Lee%2C+and+Kristina+Toutanova%2E+2019%2E+BERT%3A+Pre%2Dtraining+of+deep+bidirectional+transformers+for+language+understanding%2E+In+Proceedings+of+the+2019+Conference+of+the+N)
11. Jiachen Du, Ruifeng Xu, Yulan He, and Lin Gui. 2017. Stance classification with target-specific neural attention networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pages 3988–3994. [find](https://scholar.google.com/scholar?q=Jiachen+Du%2C+Ruifeng+Xu%2C+Yulan+He%2C+and+Lin+Gui%2E+2017%2E+Stance+classification+with+target%2Dspecific+neural+attention+networks%2E+In+Proceedings+of+the+26th+International+Joint+Conference+on+Artificial+Intel)
12. Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar. 2018. Born again neural networks. In Proceedings of the 35th International Conference on Machine Learning, pages 1607–1616. [find](https://scholar.google.com/scholar?q=Tommaso+Furlanello%2C+Zachary+Lipton%2C+Michael+Tschannen%2C+Laurent+Itti%2C+and+Anima+Anandkumar%2E+2018%2E+Born+again+neural+networks%2E+In+Proceedings+of+the+35th+International+Conference+on+Machine+Learning%2C+pa)
13. Kyle Glandt, Sarthak Khanal, Yingjie Li, Doina Caragea, and Cornelia Caragea. 2021. Stance detection in COVID-19 tweets. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Lo… [find](https://scholar.google.com/scholar?q=Kyle+Glandt%2C+Sarthak+Khanal%2C+Yingjie+Li%2C+Doina+Caragea%2C+and+Cornelia+Caragea%2E+2021%2E+Stance+detection+in+COVID%2D19+tweets%2E+In+Proceedings+of+the+59th+Annual+Meeting+of+the+Association+for+Computational+) [our card](https://md.research.elamur.ai/free/papers/glandt-2021-stance-detection-covid-19-tweets/)
14. Genevieve Gorrell, Elena Kochkina, Maria Liakata, Ahmet Aker, Arkaitz Zubiaga, Kalina Bontcheva, and Leon Derczynski. 2019. SemEval-2019 task 7: RumourEval, determining rumour veracity and support for rumours. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 845–854. [find](https://scholar.google.com/scholar?q=Genevieve+Gorrell%2C+Elena+Kochkina%2C+Maria+Liakata%2C+Ahmet+Aker%2C+Arkaitz+Zubiaga%2C+Kalina+Bontcheva%2C+and+Leon+Derczynski%2E+2019%2E+SemEval%2D2019+task+7%3A+RumourEval%2C+determining+rumour+veracity+and+support+for)
15. Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017. On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning, pages 1321–1330. [find](https://scholar.google.com/scholar?q=Chuan+Guo%2C+Geoff+Pleiss%2C+Yu+Sun%2C+and+Kilian+Q%2E+Weinberger%2E+2017%2E+On+calibration+of+modern+neural+networks%2E+In+Proceedings+of+the+34th+International+Conference+on+Machine+Learning%2C+pages+1321%E2%80%931330%2E)
16. Han Guo, Ramakanth Pasunuru, and Mohit Bansal. 2021. An overview of uncertainty calibration for text classification and the role of distillation. In Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pages 289–306. [find](https://scholar.google.com/scholar?q=Han+Guo%2C+Ramakanth+Pasunuru%2C+and+Mohit+Bansal%2E+2021%2E+An+overview+of+uncertainty+calibration+for+text+classification+and+the+role+of+distillation%2E+In+Proceedings+of+the+6th+Workshop+on+Representation+L)
17. Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531. [find](https://scholar.google.com/scholar?q=Geoffrey+Hinton%2C+Oriol+Vinyals%2C+and+Jeff+Dean%2E+2015%2E+Distilling+the+knowledge+in+a+neural+network%2E+arXiv+preprint+arXiv%3A1503%2E02531%2E) [our card](https://md.research.elamur.ai/free/papers/hinton-2015-distilling-knowledge-neural-network/)
18. Mahshid Hosseini and Cornelia Caragea. 2021. Distilling knowledge for empathy detection. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3713–3724, Punta Cana, Dominican Republic. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Mahshid+Hosseini+and+Cornelia+Caragea%2E+2021%2E+Distilling+knowledge+for+empathy+detection%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2021%2C+pages+3713%E2%80%933724%2C+Punta+Cana%2C+Dominica)
19. Mahshid Hosseini and Cornelia Caragea. 2022. Calibrating student models for emotion-related tasks. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9266–9278, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Mahshid+Hosseini+and+Cornelia+Caragea%2E+2022%2E+Calibrating+student+models+for+emotion%2Drelated+tasks%2E+In+Proceedings+of+the+2022+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+9266)
20. Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu. 2020. TinyBERT: Distilling BERT for natural language understanding. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4163– 4174. [find](https://scholar.google.com/scholar?q=Xiaoqi+Jiao%2C+Yichun+Yin%2C+Lifeng+Shang%2C+Xin+Jiang%2C+Xiao+Chen%2C+Linlin+Li%2C+Fang+Wang%2C+and+Qun+Liu%2E+2020%2E+TinyBERT%3A+Distilling+BERT+for+natural+language+understanding%2E+In+Findings+of+the+Association+for+C)
21. Kornraphop Kawintiranon and Lisa Singh. 2021. Knowledge enhanced masked language model for stance detection. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4725–4735. [find](https://scholar.google.com/scholar?q=Kornraphop+Kawintiranon+and+Lisa+Singh%2E+2021%2E+Knowledge+enhanced+masked+language+model+for+stance+detection%2E+In+Proceedings+of+the+2021+Conference+of+the+North+American+Chapter+of+the+Association+for+) [our card](https://md.research.elamur.ai/free/papers/kawintiranon-2021-knowledge-enhanced-masked-language-model/)
22. Yoon Kim and Alexander M. Rush. 2016. Sequence-level knowledge distillation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1317–1327. [find](https://scholar.google.com/scholar?q=Yoon+Kim+and+Alexander+M%2E+Rush%2E+2016%2E+Sequence%2Dlevel+knowledge+distillation%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+1317%E2%80%931327%2E) [our card](https://md.research.elamur.ai/free/papers/kim-2016-sequence-level-knowledge-distillation/)
23. Dilek Küçük and Fazli Can. 2020. Stance detection: A survey. ACM Comput. Surv., 53(1):1–37. [find](https://scholar.google.com/scholar?q=Dilek+K%C3%BC%C3%A7%C3%BCk+and+Fazli+Can%2E+2020%2E+Stance+detection%3A+A+survey%2E+ACM+Comput%2E+Surv%2E%2C+53%281%29%3A1%E2%80%9337%2E)
24. Yingjie Li and Cornelia Caragea. 2019. Multi-task stance detection with sentiment and stance lexicons. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6298–6304. [find](https://scholar.google.com/scholar?q=Yingjie+Li+and+Cornelia+Caragea%2E+2019%2E+Multi%2Dtask+stance+detection+with+sentiment+and+stance+lexicons%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods+in+Natural+Language+Processing+and+the) [our card](https://md.research.elamur.ai/free/papers/li-2019-multi-task-stance-detection-sentiment/)
25. Yingjie Li and Cornelia Caragea. 2021a. A multi-task learning framework for multi-target stance detection. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2320–2326. [find](https://scholar.google.com/scholar?q=Yingjie+Li+and+Cornelia+Caragea%2E+2021a%2E+A+multi%2Dtask+learning+framework+for+multi%2Dtarget+stance+detection%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2DIJCNLP+2021%2C+pages+2320%E2%80%93232) [our card](https://md.research.elamur.ai/free/papers/li-2021-multi-task-learning-framework-multi/)
26. Yingjie Li and Cornelia Caragea. 2021b. Target-aware data augmentation for stance detection. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1850–1860. [find](https://scholar.google.com/scholar?q=Yingjie+Li+and+Cornelia+Caragea%2E+2021b%2E+Target%2Daware+data+augmentation+for+stance+detection%2E+In+Proceedings+of+the+2021+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Li) [our card](https://md.research.elamur.ai/free/papers/li-2021-target-aware-data-augmentation-stance/)
27. Yingjie Li, Tiberiu Sosea, Aditya Sawant, Ajith Ja-yaraman Nair, Diana Inkpen, and Cornelia Caragea. 2021a. P-stance: A large dataset for stance detection in political domain. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2355–2365. [find](https://scholar.google.com/scholar?q=Yingjie+Li%2C+Tiberiu+Sosea%2C+Aditya+Sawant%2C+Ajith+Ja%2Dyaraman+Nair%2C+Diana+Inkpen%2C+and+Cornelia+Caragea%2E+2021a%2E+P%2Dstance%3A+A+large+dataset+for+stance+detection+in+political+domain%2E+In+Findings+of+the+Assoc) [our card](https://md.research.elamur.ai/free/papers/li-2021-p-stance-large-dataset-stance/)
28. Yingjie Li, Chenye Zhao, and Cornelia Caragea. 2021b. Improving stance detection with multi-dataset learning and knowledge distillation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6332–6345. [find](https://scholar.google.com/scholar?q=Yingjie+Li%2C+Chenye+Zhao%2C+and+Cornelia+Caragea%2E+2021b%2E+Improving+stance+detection+with+multi%2Ddataset+learning+and+knowledge+distillation%2E+In+Proceedings+of+the+2021+Conference+on+Empirical+Methods+in+N) [our card](https://md.research.elamur.ai/free/papers/li-2021-improving-stance-detection-multi-dataset/)
29. Yingjie Li, Chenye Zhao, and Cornelia Caragea. 2023. Tts: A target-based teacher-student framework for zero-shot stance detection. In Proceedings of the ACM Web Conference 2023, page 1500–1509. [find](https://scholar.google.com/scholar?q=Yingjie+Li%2C+Chenye+Zhao%2C+and+Cornelia+Caragea%2E+2023%2E+Tts%3A+A+target%2Dbased+teacher%2Dstudent+framework+for+zero%2Dshot+stance+detection%2E+In+Proceedings+of+the+ACM+Web+Conference+2023%2C+page+1500%E2%80%931509%2E)
30. Bin Liang, Zixiao Chen, Lin Gui, Yulan He, Min Yang, and Ruifeng Xu. 2022a. Zero-shot stance detection via contrastive learning. In Proceedings of the ACM Web Conference 2022, page 2738–2747. [find](https://scholar.google.com/scholar?q=Bin+Liang%2C+Zixiao+Chen%2C+Lin+Gui%2C+Yulan+He%2C+Min+Yang%2C+and+Ruifeng+Xu%2E+2022a%2E+Zero%2Dshot+stance+detection+via+contrastive+learning%2E+In+Proceedings+of+the+ACM+Web+Conference+2022%2C+page+2738%E2%80%932747%2E)
31. Bin Liang, Yonghao Fu, Lin Gui, Min Yang, Jiachen Du, Yulan He, and Ruifeng Xu. 2021. Target-adaptive graph for cross-target stance detection. In Proceedings of the Web Conference 2021, page 3453–3464. [find](https://scholar.google.com/scholar?q=Bin+Liang%2C+Yonghao+Fu%2C+Lin+Gui%2C+Min+Yang%2C+Jiachen+Du%2C+Yulan+He%2C+and+Ruifeng+Xu%2E+2021%2E+Target%2Dadaptive+graph+for+cross%2Dtarget+stance+detection%2E+In+Proceedings+of+the+Web+Conference+2021%2C+page+3453%E2%80%933464)
32. Bin Liang, Qinglin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, and Ruifeng Xu. 2022b. JointCL: A joint contrastive learning framework for zero-shot stance detection. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 81–91. [find](https://scholar.google.com/scholar?q=Bin+Liang%2C+Qinglin+Zhu%2C+Xiang+Li%2C+Min+Yang%2C+Lin+Gui%2C+Yulan+He%2C+and+Ruifeng+Xu%2E+2022b%2E+JointCL%3A+A+joint+contrastive+learning+framework+for+zero%2Dshot+stance+detection%2E+In+Proceedings+of+the+60th+Annual+) [our card](https://md.research.elamur.ai/free/papers/liang-2022-jointcl-joint-contrastive-learning-framework/)
33. Rui Liu, Zheng Lin, Yutong Tan, and Weiping Wang. 2021a. Enhancing zero-shot and few-shot stance detection with commonsense knowledge graph. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 3152–3157. [find](https://scholar.google.com/scholar?q=Rui+Liu%2C+Zheng+Lin%2C+Yutong+Tan%2C+and+Weiping+Wang%2E+2021a%2E+Enhancing+zero%2Dshot+and+few%2Dshot+stance+detection+with+commonsense+knowledge+graph%2E+In+Findings+of+the+Association+for+Computational+Linguistic) [our card](https://md.research.elamur.ai/free/papers/liu-2021-enhancing-zero-shot-few-shot/)
34. Yang Liu, Sheng Shen, and Mirella Lapata. 2021b. Noisy self-knowledge distillation for text summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 692–703. [find](https://scholar.google.com/scholar?q=Yang+Liu%2C+Sheng+Shen%2C+and+Mirella+Lapata%2E+2021b%2E+Noisy+self%2Dknowledge+distillation+for+text+summarization%2E+In+Proceedings+of+the+2021+Conference+of+the+North+American+Chapter+of+the+Association+for+Co)
35. Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Ilya+Loshchilov+and+Frank+Hutter%2E+2019%2E+Decoupled+weight+decay+regularization%2E+In+International+Conference+on+Learning+Representations%2E)
36. Hossein Mobahi, Mehrdad Farajtabar, and Peter Bartlett. 2020. Self-distillation amplifies regularization in hilbert space. In Advances in Neural Information Processing Systems, volume 33, pages 3351–3361. [find](https://scholar.google.com/scholar?q=Hossein+Mobahi%2C+Mehrdad+Farajtabar%2C+and+Peter+Bartlett%2E+2020%2E+Self%2Ddistillation+amplifies+regularization+in+hilbert+space%2E+In+Advances+in+Neural+Information+Processing+Systems%2C+volume+33%2C+pages+3351%E2%80%933)
37. Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiao-Dan Zhu, and Colin Cherry. 2016a. A dataset for detecting stance in tweets. In LREC. [find](https://scholar.google.com/scholar?q=Saif+Mohammad%2C+Svetlana+Kiritchenko%2C+Parinaz+Sobhani%2C+Xiao%2DDan+Zhu%2C+and+Colin+Cherry%2E+2016a%2E+A+dataset+for+detecting+stance+in+tweets%2E+In+LREC%2E)
38. Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016b. Semeval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 31–41. [find](https://scholar.google.com/scholar?q=Saif+Mohammad%2C+Svetlana+Kiritchenko%2C+Parinaz+Sobhani%2C+Xiaodan+Zhu%2C+and+Colin+Cherry%2E+2016b%2E+Semeval%2D2016+task+6%3A+Detecting+stance+in+tweets%2E+In+Proceedings+of+the+10th+International+Workshop+on+Semant)
39. Saif M Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. 2017. Stance and sentiment in tweets. ACM Transactions on Internet Technology (TOIT), 17(3):26. [find](https://scholar.google.com/scholar?q=Saif+M+Mohammad%2C+Parinaz+Sobhani%2C+and+Svetlana+Kiritchenko%2E+2017%2E+Stance+and+sentiment+in+tweets%2E+ACM+Transactions+on+Internet+Technology+%28TOIT%29%2C+17%283%29%3A26%2E)
40. Mahdi Pakdaman Naeini, Gregory F Cooper, and Milos Hauskrecht. 2015. Obtaining well calibrated probabilities using bayesian binning. In AAAI, pages 2901–2907. [find](https://scholar.google.com/scholar?q=Mahdi+Pakdaman+Naeini%2C+Gregory+F+Cooper%2C+and+Milos+Hauskrecht%2E+2015%2E+Obtaining+well+calibrated+probabilities+using+bayesian+binning%2E+In+AAAI%2C+pages+2901%E2%80%932907%2E)
41. Dat Quoc Nguyen, Thanh Vu, and Anh Tuan Nguyen. 2020. BERTweet: A pre-trained language model for English tweets. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 9–14. [find](https://scholar.google.com/scholar?q=Dat+Quoc+Nguyen%2C+Thanh+Vu%2C+and+Anh+Tuan+Nguyen%2E+2020%2E+BERTweet%3A+A+pre%2Dtrained+language+model+for+English+tweets%2E+In+Proceedings+of+the+2020+Conference+on+Empirical+Methods+in+Natural+Language+Processi) [our card](https://md.research.elamur.ai/free/papers/nguyen-2020-bertweet-pre-trained-language-model/)
42. Alexandru Niculescu-Mizil and Rich Caruana. 2005. Predicting good probabilities with supervised learning. In Proceedings of the 22nd International Conference on Machine Learning, page 625–632. [find](https://scholar.google.com/scholar?q=Alexandru+Niculescu%2DMizil+and+Rich+Caruana%2E+2005%2E+Predicting+good+probabilities+with+supervised+learning%2E+In+Proceedings+of+the+22nd+International+Conference+on+Machine+Learning%2C+page+625%E2%80%93632%2E)
43. Seo Yeon Park and Cornelia Caragea. 2022a. A data cartography based MixUp for pre-trained language models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4244–4250, Seattle, United States. Assoc… [find](https://scholar.google.com/scholar?q=Seo+Yeon+Park+and+Cornelia+Caragea%2E+2022a%2E+A+data+cartography+based+MixUp+for+pre%2Dtrained+language+models%2E+In+Proceedings+of+the+2022+Conference+of+the+North+American+Chapter+of+the+Association+for+Co)
44. Seo Yeon Park and Cornelia Caragea. 2022b. On the calibration of pre-trained language models using mixup guided by area under the margin and saliency. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5364–5374, Dublin, Ireland.… [find](https://scholar.google.com/scholar?q=Seo+Yeon+Park+and+Cornelia+Caragea%2E+2022b%2E+On+the+calibration+of+pre%2Dtrained+language+models+using+mixup+guided+by+area+under+the+margin+and+saliency%2E+In+Proceedings+of+the+60th+Annual+Meeting+of+the+)
45. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Te-jani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and… [find](https://scholar.google.com/scholar?q=Adam+Paszke%2C+Sam+Gross%2C+Francisco+Massa%2C+Adam+Lerer%2C+James+Bradbury%2C+Gregory+Chanan%2C+Trevor+Killeen%2C+Zeming+Lin%2C+Natalia+Gimelshein%2C+Luca+Antiga%2C+Alban+Desmaison%2C+Andreas+Kopf%2C+Edward+Yang%2C+Zachary+De) [our card](https://md.research.elamur.ai/free/papers/paszke-2019-pytorch-imperative-style-high-performance/)
46. John C. Platt. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers, pages 61–74. [find](https://scholar.google.com/scholar?q=John+C%2E+Platt%2E+1999%2E+Probabilistic+outputs+for+support+vector+machines+and+comparisons+to+regularized+likelihood+methods%2E+In+Advances+in+Large+Margin+Classifiers%2C+pages+61%E2%80%9374%2E)
47. Benjamin Schiller, Johannes Daxenberger, and Iryna Gurevych. 2021. Stance detection benchmark: How robust is your stance detection? KI - Künstliche Intelligenz. [find](https://scholar.google.com/scholar?q=Benjamin+Schiller%2C+Johannes+Daxenberger%2C+and+Iryna+Gurevych%2E+2021%2E+Stance+detection+benchmark%3A+How+robust+is+your+stance+detection%3F+KI+%2D+K%C3%BCnstliche+Intelligenz%2E)
48. Umme Aymun Siddiqua, Abu Nowshed Chy, and Masaki Aono. 2019. Tweet stance detection using an attention based neural ensemble model. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Sh… [find](https://scholar.google.com/scholar?q=Umme+Aymun+Siddiqua%2C+Abu+Nowshed+Chy%2C+and+Masaki+Aono%2E+2019%2E+Tweet+stance+detection+using+an+attention+based+neural+ensemble+model%2E+In+Proceedings+of+the+2019+Conference+of+the+North+American+Chapter+) [our card](https://md.research.elamur.ai/free/papers/siddiqua-2019-tweet-stance-detection-attention-based/)
49. Parinaz Sobhani, Diana Inkpen, and Xiaodan Zhu. 2017. A dataset for multi-target stance detection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 551–557. [find](https://scholar.google.com/scholar?q=Parinaz+Sobhani%2C+Diana+Inkpen%2C+and+Xiaodan+Zhu%2E+2017%2E+A+dataset+for+multi%2Dtarget+stance+detection%2E+In+Proceedings+of+the+15th+Conference+of+the+European+Chapter+of+the+Association+for+Computational+Li) [our card](https://md.research.elamur.ai/free/papers/sobhani-2017-dataset-multi-target-stance-detection/)
50. Christian Stab, Tristan Miller, Benjamin Schiller, Pranav Rai, and Iryna Gurevych. 2018. Cross-topic argument mining from heterogeneous sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3664– 3674. [find](https://scholar.google.com/scholar?q=Christian+Stab%2C+Tristan+Miller%2C+Benjamin+Schiller%2C+Pranav+Rai%2C+and+Iryna+Gurevych%2E+2018%2E+Cross%2Dtopic+argument+mining+from+heterogeneous+sources%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Meth)
51. Qingying Sun, Zhongqing Wang, Qiaoming Zhu, and Guodong Zhou. 2018. Stance detection with hierarchical attention network. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2399–2409. [find](https://scholar.google.com/scholar?q=Qingying+Sun%2C+Zhongqing+Wang%2C+Qiaoming+Zhu%2C+and+Guodong+Zhou%2E+2018%2E+Stance+detection+with+hierarchical+attention+network%2E+In+Proceedings+of+the+27th+International+Conference+on+Computational+Linguisti) [our card](https://md.research.elamur.ai/free/papers/sun-2018-stance-detection-hierarchical-attention-network/)
52. Siqi Sun, Yu Cheng, Zhe Gan, and Jingjing Liu. 2019. Patient knowledge distillation for BERT model compression. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 43… [find](https://scholar.google.com/scholar?q=Siqi+Sun%2C+Yu+Cheng%2C+Zhe+Gan%2C+and+Jingjing+Liu%2E+2019%2E+Patient+knowledge+distillation+for+BERT+model+compression%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods+in+Natural+Language+Processin) [our card](https://md.research.elamur.ai/free/papers/sun-2019-patient-knowledge-distillation-bert-model/)
53. Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, pages 2818–2826. [find](https://scholar.google.com/scholar?q=Christian+Szegedy%2C+Vincent+Vanhoucke%2C+Sergey+Ioffe%2C+Jonathon+Shlens%2C+and+Zbigniew+Wojna%2E+2016%2E+Rethinking+the+inception+architecture+for+computer+vision%2E+In+2016+IEEE+Conference+on+Computer+Vision+and)
54. Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, and Jun Xie. 2020. Improving event detection via open-domain trigger knowledge. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5887–5897. [find](https://scholar.google.com/scholar?q=Meihan+Tong%2C+Bin+Xu%2C+Shuai+Wang%2C+Yixin+Cao%2C+Lei+Hou%2C+Juanzi+Li%2C+and+Jun+Xie%2E+2020%2E+Improving+event+detection+via+open%2Ddomain+trigger+knowledge%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+the+Associa)
55. Penghui Wei, Junjie Lin, and Wenji Mao. 2018. Multi-target stance detection via a dynamic memory-augmented network. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018, pages 1229–1232. [find](https://scholar.google.com/scholar?q=Penghui+Wei%2C+Junjie+Lin%2C+and+Wenji+Mao%2E+2018%2E+Multi%2Dtarget+stance+detection+via+a+dynamic+memory%2Daugmented+network%2E+In+The+41st+International+ACM+SIGIR+Conference+on+Research+%26+Development+in+Informat)
56. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, Remi Louf, Morgan Funtow-icz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lh… [find](https://scholar.google.com/scholar?q=Thomas+Wolf%2C+Lysandre+Debut%2C+Victor+Sanh%2C+Julien+Chaumond%2C+Clement+Delangue%2C+Anthony+Moi%2C+Pier%2Dric+Cistac%2C+Tim+Rault%2C+Remi+Louf%2C+Morgan+Funtow%2Dicz%2C+Joe+Davison%2C+Sam+Shleifer%2C+Patrick+von+Platen%2C+Clara) [our card](https://md.research.elamur.ai/free/papers/wolf-2020-transformers-state-art-natural-language/)