---
title: "A Multi-Task Learning Framework for Multi-Target — Li 2021"
description: "Multi-target stance detection (MTSD) asks a model to decide, for a single tweet, whether its author is FAVOR, AGAINST or NONE toward each of two targets."
authors: ["Yingjie Li", "Cornelia Caragea"]
year: 2021
venue: "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021"
source: "https://aclanthology.org/2021.findings-acl.204/"
doi: "10.18653/v1/2021.findings-acl.204"
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-2021-multi-task-learning-framework-multi/"
---
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# A Multi-Task Learning Framework for Multi-Target 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:** 2021 · **Venue:** Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.findings-acl.204/) · [PDF](https://aclanthology.org/2021.findings-acl.204.pdf) · [DOI 10.18653/v1/2021.findings-acl.204](https://doi.org/10.18653/v1/2021.findings-acl.204) · [OpenAlex](https://openalex.org/W3176823731)

**Citations:** 21 citations (openalex-snapshot · 2026-10-14) · [10](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Multi-target stance detection (MTSD) asks a model to decide, for a single tweet, whether its author is FAVOR, AGAINST or NONE toward each of two targets. Earlier systems trained a separate model for every target pair ("Ad-hoc"). The authors argue this setup lets models rely on surface words instead of target cues, which leads to overfitting.

They propose two changes. The first is a "Merged" strategy, where one model learns from the data of all target pairs but is still scored on each pair separately. The second is a multi-task network built on a fine-tuned BERTweet. It adds an auxiliary binary task that checks whether the author holds the same stance toward both targets. This task uses the concatenated [CLS] vectors of two target-conditioned inputs.

Under Merged training, classic baselines lose a lot of accuracy, while BERTweet gains. The combined model, BERTweet-A, beats the strongest baseline by 12.39 macro-F1 points. The work therefore offers both a tougher evaluation protocol for MTSD and a stronger model.

## Key points
- **Data:** the multi-target tweet dataset of Sobhani et al. (2017) has 4,455 tweets split 3,119/446/890 for train/dev/test. It covers three pairs: Trump–Clinton, Trump–Cruz and Clinton–Sanders.
- **Effect on baselines:** switching to Merged training cuts F_macro by 10.28 points for BiLSTM and 12.64 points for CNN, a sign that both had memorized pair-specific patterns.
- **BERTweet gains:** BERTweet rises from 59.51 to 67.77 F_macro with Merged training. Adding the (dis)agreement task lifts it to 69.65. The auxiliary loss weight α is 0.5.
- **Generalization test:** on the "Donald Trump" target of SemEval 2016, BERTweet-merged scores 52.51 versus 46.75 for BERTweet-adhoc.
- **Code:** released at github.com/chuchun8/MTSD.

*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

10 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 5 not yet.

- [Can Large Language Models Address Open-Target Stance Detection?](https://md.research.elamur.ai/free/papers/akash-2025-can-large-language-models-address/)
- [Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation](https://md.research.elamur.ai/free/papers/li-2021-improving-stance-detection-multi-dataset/)
- [Distilling Calibrated Knowledge for Stance Detection](https://md.research.elamur.ai/free/papers/li-2023-distilling-calibrated-knowledge-stance-detection/)
- [A New Direction in Stance Detection: Target-Stance Extraction in the Wild](https://md.research.elamur.ai/free/papers/li-2023-new-direction-stance-detection-target/)
- [C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection](https://md.research.elamur.ai/free/papers/zhao-2023-c-stance-large-dataset-chinese/)

## Cite

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

```bibtex
@inproceedings{li2021multi,
  title = {{A Multi-Task Learning Framework for Multi-Target Stance Detection}},
  author = {Li, Yingjie and Caragea, Cornelia},
  booktitle = {Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
  year = {2021},
  doi = {10.18653/v1/2021.findings-acl.204},
  url = {https://doi.org/10.18653/v1/2021.findings-acl.204}
}
```

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## References

28 references, 10 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Abeer ALDayel and Walid Magdy. 2021. Stance detection on social media: State of the art and trends. Information Processing & Management, 58(4):102597. [find](https://scholar.google.com/scholar?q=Abeer+ALDayel+and+Walid+Magdy%2E+2021%2E+Stance+detection+on+social+media%3A+State+of+the+art+and+trends%2E+Information+Processing+%26+Management%2C+58%284%29%3A102597%2E)
2. 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+Rockta%CC%88schel%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/)
3. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In 3rd International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Dzmitry+Bahdanau%2C+Kyunghyun+Cho%2C+and+Yoshua+Bengio%2E+2015%2E+Neural+machine+translation+by+jointly+learning+to+align+and+translate%2E+In+3rd+International+Conference+on+Learning+Representations%2E) [our card](https://md.research.elamur.ai/free/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
4. 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)
5. 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)
6. Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, and Roger Zimmermann. 2018. Modeling inter-aspect dependencies for aspect-based sentiment analysis. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Lingui… [find](https://scholar.google.com/scholar?q=Devamanyu+Hazarika%2C+Soujanya+Poria%2C+Prateek+Vij%2C+Gangeshwar+Krishnamurthy%2C+Erik+Cambria%2C+and+Roger+Zimmermann%2E+2018%2E+Modeling+inter%2Daspect+dependencies+for+aspect%2Dbased+sentiment+analysis%2E+In+Proceedi)
7. Binxuan Huang and Kathleen Carley. 2018. Parameterized convolutional neural networks for aspect level sentiment classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1091–1096. [find](https://scholar.google.com/scholar?q=Binxuan+Huang+and+Kathleen+Carley%2E+2018%2E+Parameterized+convolutional+neural+networks+for+aspect+level+sentiment+classification%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Methods+in+Natural+La)
8. Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746–1751. [find](https://scholar.google.com/scholar?q=Yoon+Kim%2E+2014%2E+Convolutional+neural+networks+for+sentence+classification%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2C+pages+1746%E2%80%931751%2E) [our card](https://md.research.elamur.ai/free/papers/kim-2014-convolutional-neural-networks-sentence-classification/)
9. 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+Ku%CC%88c%CC%A7u%CC%88k+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)
10. 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/)
11. Yingjie Li and Cornelia Caragea. 2021. 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+2021%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+Lin) [our card](https://md.research.elamur.ai/free/papers/li-2021-target-aware-data-augmentation-stance/)
12. Peiqin Lin, Meng Yang, and Jianhuang Lai. 2019. Deep mask memory network with semantic dependency and context moment for aspect level sentiment classification. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pages 5088–5094. [find](https://scholar.google.com/scholar?q=Peiqin+Lin%2C+Meng+Yang%2C+and+Jianhuang+Lai%2E+2019%2E+Deep+mask+memory+network+with+semantic+dependency+and+context+moment+for+aspect+level+sentiment+classification%2E+In+Proceedings+of+the+Twenty%2DEighth+Inte)
13. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. [find](https://scholar.google.com/scholar?q=Yinhan+Liu%2C+Myle+Ott%2C+Naman+Goyal%2C+Jingfei+Du%2C+Mandar+Joshi%2C+Danqi+Chen%2C+Omer+Levy%2C+Mike+Lewis%2C+Luke+Zettlemoyer%2C+and+Veselin+Stoyanov%2E+2019%2E+Roberta%3A+A+robustly+optimized+bert+pretraining+approach%2E+a)
14. 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)
15. Navonil Majumder, Soujanya Poria, Alexander Gelbukh, Md. Shad Akhtar, Erik Cambria, and Asif Ekbal. 2018. IARM: Inter-aspect relation modeling with memory networks in aspect-based sentiment analysis. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 340… [find](https://scholar.google.com/scholar?q=Navonil+Majumder%2C+Soujanya+Poria%2C+Alexander+Gelbukh%2C+Md%2E+Shad+Akhtar%2C+Erik+Cambria%2C+and+Asif+Ekbal%2E+2018%2E+IARM%3A+Inter%2Daspect+relation+modeling+with+memory+networks+in+aspect%2Dbased+sentiment+analysis%2E+)
16. Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016. 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+2016%2E+Semeval%2D2016+task+6%3A+Detecting+stance+in+tweets%2E+In+Proceedings+of+the+10th+International+Workshop+on+Semanti)
17. 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/)
18. Mike Schuster and Kuldip K Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673–2681. [find](https://scholar.google.com/scholar?q=Mike+Schuster+and+Kuldip+K+Paliwal%2E+1997%2E+Bidirectional+recurrent+neural+networks%2E+IEEE+Transactions+on+Signal+Processing%2C+45%2811%29%3A2673%E2%80%932681%2E)
19. 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/)
20. Parinaz Sobhani, Saif Mohammad, and Svetlana Kiritchenko. 2016. Detecting stance in tweets and analyzing its interaction with sentiment. In Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics, pages 159–169. [find](https://scholar.google.com/scholar?q=Parinaz+Sobhani%2C+Saif+Mohammad%2C+and+Svetlana+Kiritchenko%2E+2016%2E+Detecting+stance+in+tweets+and+analyzing+its+interaction+with+sentiment%2E+In+Proceedings+of+the+Fifth+Joint+Conference+on+Lexical+and+Com)
21. Youwei Song, Jiahai Wang, Tao Jiang, Zhiyue Liu, and Yanghui Rao. 2019. Attentional encoder network for targeted sentiment classification. arXiv preprint arXiv:1902.09314. — [arXiv:1902.09314](https://arxiv.org/abs/1902.09314) [find](https://scholar.google.com/scholar?q=Youwei+Song%2C+Jiahai+Wang%2C+Tao+Jiang%2C+Zhiyue+Liu%2C+and+Yanghui+Rao%2E+2019%2E+Attentional+encoder+network+for+targeted+sentiment+classification%2E+arXiv+preprint+arXiv%3A1902%2E09314%2E+%E2%80%94+%5BarXiv%3A1902%2E09314%5D%28https%3A%2F)
22. 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/)
23. Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, and Deb Roy. 2016. DeepStance at SemEval-2016 task 6: Detecting stance in tweets using character and word-level CNNs. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 413–419. [find](https://scholar.google.com/scholar?q=Prashanth+Vijayaraghavan%2C+Ivan+Sysoev%2C+Soroush+Vosoughi%2C+and+Deb+Roy%2E+2016%2E+DeepStance+at+SemEval%2D2016+task+6%3A+Detecting+stance+in+tweets+using+character+and+word%2Dlevel+CNNs%2E+In+Proceedings+of+the+10t)
24. Penghui Wei, Junjie Lin, and Wenji Mao. 2018a. 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+2018a%2E+Multi%2Dtarget+stance+detection+via+a+dynamic+memory%2Daugmented+network%2E+In+The+41st+International+ACM+SIGIR+Conference+on+Research+%26+Development+in+Informa)
25. Penghui Wei, Wenji Mao, and Daniel Zeng. 2018b. A target-guided neural memory model for stance detection in twitter. In 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018, pages 1–8. [find](https://scholar.google.com/scholar?q=Penghui+Wei%2C+Wenji+Mao%2C+and+Daniel+Zeng%2E+2018b%2E+A+target%2Dguided+neural+memory+model+for+stance+detection+in+twitter%2E+In+2018+International+Joint+Conference+on+Neural+Networks%2C+IJCNN+2018%2C+Rio+de+Janei)
26. Wan Wei, Xiao Zhang, Xuqin Liu, Wei Chen, and Tengjiao Wang. 2016. pkudblab at SemEval-2016 task 6: A specific convolutional neural network system for effective stance detection. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 384–388. [find](https://scholar.google.com/scholar?q=Wan+Wei%2C+Xiao+Zhang%2C+Xuqin+Liu%2C+Wei+Chen%2C+and+Tengjiao+Wang%2E+2016%2E+pkudblab+at+SemEval%2D2016+task+6%3A+A+specific+convolutional+neural+network+system+for+effective+stance+detection%2E+In+Proceedings+of+the) [our card](https://md.research.elamur.ai/free/papers/wei-2016-pkudblab-semeval-2016-task-6/)
27. Wei Xue and Tao Li. 2018. Aspect based sentiment analysis with gated convolutional networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2514–2523. [find](https://scholar.google.com/scholar?q=Wei+Xue+and+Tao+Li%2E+2018%2E+Aspect+based+sentiment+analysis+with+gated+convolutional+networks%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+)
28. Guido Zarrella and Amy Marsh. 2016. MITRE at SemEval-2016 task 6: Transfer learning for stance detection. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 458–463. [find](https://scholar.google.com/scholar?q=Guido+Zarrella+and+Amy+Marsh%2E+2016%2E+MITRE+at+SemEval%2D2016+task+6%3A+Transfer+learning+for+stance+detection%2E+In+Proceedings+of+the+10th+International+Workshop+on+Semantic+Evaluation+%28SemEval%2D2016%29%2C+pages) [our card](https://md.research.elamur.ai/free/papers/zarrella-2016-mitre-semeval-2016-task-6/)