---
title: "Acquired TASTE — Barel 2025"
description: "The paper addresses stance detection: determining whether a speaker supports a given claim or opposes it."
authors: ["Guy Barel", "Oren Tsur", "Dan Vilenchik"]
year: 2025
venue: "Proceedings of the 31st International Conference on Computational Linguistics"
source: "https://aclanthology.org/2025.coling-main.433/"
arxiv: "2412.03681"
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/barel-2025-acquired-taste-multimodal-stance-detection/"
---
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# Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings

**Authors:** Guy Barel, [Oren Tsur](https://md.research.elamur.ai/free/authors/oren-tsur/), Dan Vilenchik · **Year:** 2025 · **Venue:** Proceedings of the 31st International Conference on Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.coling-main.433/) · [PDF](https://aclanthology.org/2025.coling-main.433.pdf) · [arXiv 2412.03681](https://arxiv.org/abs/2412.03681) · [OpenAlex](https://openalex.org/W4405096532)

**Citations:** 1 citations (openalex-snapshot · 2026-10-14) · 7 in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses stance detection: determining whether a speaker supports a given claim or opposes it. The task is solved for multi-party discussions at two levels: the individual post and the author as a whole. The authors propose TASTE, a multimodal architecture with two modalities, textual and structural. The post text is encoded by a fine-tuned Sentence-BERT. The structural representation is built in an unsupervised manner. The discussion tree is converted into a graph of interactions between speakers, and speaker vectors are obtained via an SDP relaxation of the max-cut problem. The two vectors are combined by a Gated Residual Network (GRN) layer, and an MLP classifier produces the prediction. An author's stance is determined by majority voting over their posts. On the 4Forums and CreateDebate corpora, the model outperforms a number of strong baselines, including the previous state-of-the-art. The analysis shows that conversation structure conveys the stance signal with less noise than the text itself. The most stable result comes from combining both sources. Thus, the social context of a conversation is worth taking into account in discussion analysis.

## Key points
- The interaction graph is built following Pick et al. (2022): the nodes are speakers. The edge weight is a weighted sum of replies and quotations between two participants, with hyperparameters α and β. Speaker vectors are obtained as the solution of the Goemans–Williamson SDP on the unit sphere.
- The text vector is the [CLS] representation from Sentence-BERT. The modalities are combined by a GRN (Lim et al., 2021), originally created for time-series forecasting. It performs well on small, noisy data. The modalities are fused during training (joint fusion) rather than at the model input (early fusion).
- In most cases, the structure-only model outperforms purely textual models. Adding text to structure increases accuracy by 12% on average.
- Structural models are stronger where there are many interactions and the utterances are short. Textual models work better when the texts are long and there are few interactions. At the author level, TASTE's margin over its competitors is smaller, because majority voting smooths out some of the errors on individual posts.
- Limitations: the model was tested only on English-language debate corpora. It also assumes that an author's stance does not change over the course of a discussion. This may not hold, for example, in Reddit Change My View.

*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/)

## Cite

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

```bibtex
@inproceedings{barel2025acquired,
  title = {{Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings}},
  author = {Barel, Guy and Tsur, Oren and Vilenchik, Dan},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  eprint = {2412.03681},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2412.03681}
}
```

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## References

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

1. Adrian Benton and Mark Dredze. 2018. Using author embeddings to improve tweet stance classification. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 184–194. [find](https://scholar.google.com/scholar?q=Adrian+Benton+and+Mark+Dredze%2E+2018%2E+Using+author+embeddings+to+improve+tweet+stance+classification%2E+In+Proceedings+of+the+2018+EMNLP+Workshop+W%2DNUT%3A+The+4th+Workshop+on+Noisy+User%2Dgenerated+Text%2C+pag)
2. Douglas Biber and Edward Finegan. 1988. Adverbial stance types in english. Discourse processes, 11(1):1– 34. [find](https://scholar.google.com/scholar?q=Douglas+Biber+and+Edward+Finegan%2E+1988%2E+Adverbial+stance+types+in+english%2E+Discourse+processes%2C+11%281%29%3A1%E2%80%93+34%2E)
3. Khyathi Raghavi Chandu, Yonatan Bisk, and Alan W Black. 2021. Grounding ‘grounding’in nlp. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 4283–4305. [find](https://scholar.google.com/scholar?q=Khyathi+Raghavi+Chandu%2C+Yonatan+Bisk%2C+and+Alan+W+Black%2E+2021%2E+Grounding+%E2%80%98grounding%E2%80%99in+nlp%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2DIJCNLP+2021%2C+pages+4283%E2%80%934305%2E)
4. Herbert H Clark and Susan E Brennan. 1991. Grounding in communication. [find](https://scholar.google.com/scholar?q=Herbert+H+Clark+and+Susan+E+Brennan%2E+1991%2E+Grounding+in+communication%2E)
5. Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2020. Will-they-won’t-they: A very large dataset for stance detection on twitter. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1715… [find](https://scholar.google.com/scholar?q=Costanza+Conforti%2C+Jakob+Berndt%2C+Mohammad+Taher+Pilehvar%2C+Chryssi+Giannitsarou%2C+Flavio+Toxvaerd%2C+and+Nigel+Collier%2E+2020%2E+Will%2Dthey%2Dwon%E2%80%99t%2Dthey%3A+A+very+large+dataset+for+stance+detection+on+twitter%2E+In) [our card](https://md.research.elamur.ai/free/papers/conforti-2020-will-they-won-t-they/)
6. Ming Ding, Wendi Zheng, Wenyi Hong, and Jie Tang. 2022. Cogview2: Faster and better text-to-image generation via hierarchical transformers. Advances in Neural Information Processing Systems, 35:16890– 16902. [find](https://scholar.google.com/scholar?q=Ming+Ding%2C+Wendi+Zheng%2C+Wenyi+Hong%2C+and+Jie+Tang%2E+2022%2E+Cogview2%3A+Faster+and+better+text%2Dto%2Dimage+generation+via+hierarchical+transformers%2E+Advances+in+Neural+Information+Processing+Systems%2C+35%3A16890%E2%80%93)
7. Heba Elfardy and Mona Diab. 2016. CU-GWU perspective at SemEval-2016 task 6: Ideological stance detection in informal text. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 434–439, San Diego, California. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Heba+Elfardy+and+Mona+Diab%2E+2016%2E+CU%2DGWU+perspective+at+SemEval%2D2016+task+6%3A+Ideological+stance+detection+in+informal+text%2E+In+Proceedings+of+the+10th+International+Workshop+on+Semantic+Evaluation+%28Se)
8. Michel X. Goemans and David P. Williamson. 1995. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM, 42(6):1115–1145. [find](https://scholar.google.com/scholar?q=Michel+X%2E+Goemans+and+David+P%2E+Williamson%2E+1995%2E+Improved+approximation+algorithms+for+maximum+cut+and+satisfiability+problems+using+semidefinite+programming%2E+J%2E+ACM%2C+42%286%29%3A1115%E2%80%931145%2E)
9. Rob Abbott, Brian Ecker, Pranav Anand, and Marilyn Walker. 2016. Internet argument corpus 2.0: An sql schema for dialogic social media and the corpora to go with it. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 4445–4452. [find](https://scholar.google.com/scholar?q=Rob+Abbott%2C+Brian+Ecker%2C+Pranav+Anand%2C+and+Marilyn+Walker%2E+2016%2E+Internet+argument+corpus+2%2E0%3A+An+sql+schema+for+dialogic+social+media+and+the+corpora+to+go+with+it%2E+In+Proceedings+of+the+Tenth+Intern)
10. Erving Goffman. 1955. On face-work: An analysis of ritual elements in social interaction. Psychiatry, 18(3). [find](https://scholar.google.com/scholar?q=Erving+Goffman%2E+1955%2E+On+face%2Dwork%3A+An+analysis+of+ritual+elements+in+social+interaction%2E+Psychiatry%2C+18%283%29%2E)
11. Abeer Aldayel and Walid Magdy. 2019. Your stance is exposed! analysing possible factors for stance detection on social media. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW):1–20. [find](https://scholar.google.com/scholar?q=Abeer+Aldayel+and+Walid+Magdy%2E+2019%2E+Your+stance+is+exposed%21+analysing+possible+factors+for+stance+detection+on+social+media%2E+Proceedings+of+the+ACM+on+Human%2DComputer+Interaction%2C+3%28CSCW%29%3A1%E2%80%9320%2E)
12. H Paul Grice. 1975. Logic and conversation. 1975, pages 41–58. [find](https://scholar.google.com/scholar?q=H+Paul+Grice%2E+1975%2E+Logic+and+conversation%2E+1975%2C+pages+41%E2%80%9358%2E)
13. 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/)
14. Emily Allaway and Kathleen McKeown. 2023. Zero-shot stance detection: Paradigms and challenges. Frontiers in Artificial Intelligence, 5:1070429. [find](https://scholar.google.com/scholar?q=Emily+Allaway+and+Kathleen+McKeown%2E+2023%2E+Zero%2Dshot+stance+detection%3A+Paradigms+and+challenges%2E+Frontiers+in+Artificial+Intelligence%2C+5%3A1070429%2E)
15. Erving Goffman. 1978. Response cries. Language, pages 787–815. [find](https://scholar.google.com/scholar?q=Erving+Goffman%2E+1978%2E+Response+cries%2E+Language%2C+pages+787%E2%80%93815%2E)
16. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855–864. [find](https://scholar.google.com/scholar?q=Aditya+Grover+and+Jure+Leskovec%2E+2016%2E+node2vec%3A+Scalable+feature+learning+for+networks%2E+In+Proceedings+of+the+22nd+ACM+SIGKDD+international+conference+on+Knowledge+discovery+and+data+mining%2C+pages+85)
17. Stevan Harnad. 1990. The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1-3):335–346. [find](https://scholar.google.com/scholar?q=Stevan+Harnad%2E+1990%2E+The+symbol+grounding+problem%2E+Physica+D%3A+Nonlinear+Phenomena%2C+42%281%2D3%29%3A335%E2%80%93346%2E)
18. Kazi Saidul Hasan and Vincent Ng. 2014. Why are you taking this stance? identifying and classifying reasons in ideological debates. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 751–762, Doha, Qatar. Association for Computational Linguistics… [find](https://scholar.google.com/scholar?q=Kazi+Saidul+Hasan+and+Vincent+Ng%2E+2014%2E+Why+are+you+taking+this+stance%3F+identifying+and+classifying+reasons+in+ideological+debates%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natura)
19. Bryan Lim, Sercan Ö. Arık, Nicolas Loeff, and Tomas Pfister. 2021. Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4):1748–1764. [find](https://scholar.google.com/scholar?q=Bryan+Lim%2C+Sercan+%C3%96%2E+Ar%C4%B1k%2C+Nicolas+Loeff%2C+and+Tomas+Pfister%2E+2021%2E+Temporal+fusion+transformers+for+interpretable+multi%2Dhorizon+time+series+forecasting%2E+International+Journal+of+Forecasting%2C+37%284%29%3A174)
20. Shih-Cheng Huang, Anuj Pareek, Saeed Seyyedi, Imon Banerjee, and Matthew P Lungren. 2020. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ digital medicine, 3(1):136. [find](https://scholar.google.com/scholar?q=Shih%2DCheng+Huang%2C+Anuj+Pareek%2C+Saeed+Seyyedi%2C+Imon+Banerjee%2C+and+Matthew+P+Lungren%2E+2020%2E+Fusion+of+medical+imaging+and+electronic+health+records+using+deep+learning%3A+a+systematic+review+and+implement)
21. Rui Liu, Zheng Lin, Huishan Ji, Jiangnan Li, Peng Fu, and Weiping Wang. 2022. Target really matters: Target-aware contrastive learning and consistency regularization for few-shot stance detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6944–6954. [find](https://scholar.google.com/scholar?q=Rui+Liu%2C+Zheng+Lin%2C+Huishan+Ji%2C+Jiangnan+Li%2C+Peng+Fu%2C+and+Weiping+Wang%2E+2022%2E+Target+really+matters%3A+Target%2Daware+contrastive+learning+and+consistency+regularization+for+few%2Dshot+stance+detection%2E+In+)
22. Kristen Johnson and Dan Goldwasser. 2016. Identifying stance by analyzing political discourse on twitter. In Proceedings of the First Workshop on NLP and Computational Social Science, pages 66–75. [find](https://scholar.google.com/scholar?q=Kristen+Johnson+and+Dan+Goldwasser%2E+2016%2E+Identifying+stance+by+analyzing+political+discourse+on+twitter%2E+In+Proceedings+of+the+First+Workshop+on+NLP+and+Computational+Social+Science%2C+pages+66%E2%80%9375%2E)
23. Kenneth Joseph, Lisa Friedland, William Hobbs, David Lazer, and Oren Tsur. 2017. Constance: Modeling annotation contexts to improve stance classification. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1115–1124. [find](https://scholar.google.com/scholar?q=Kenneth+Joseph%2C+Lisa+Friedland%2C+William+Hobbs%2C+David+Lazer%2C+and+Oren+Tsur%2E+2017%2E+Constance%3A+Modeling+annotation+contexts+to+improve+stance+classification%2E+In+Proceedings+of+the+2017+Conference+on+Empi)
24. 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/)
25. Parisa Jamadi Khiabani and Arkaitz Zubiaga. 2024. Few-shot learning for cross-target stance detection by aggregating multimodal embeddings. IEEE Transactions on Computational Social Systems, 11(2):2081– 2090. [find](https://scholar.google.com/scholar?q=Parisa+Jamadi+Khiabani+and+Arkaitz+Zubiaga%2E+2024%2E+Few%2Dshot+learning+for+cross%2Dtarget+stance+detection+by+aggregating+multimodal+embeddings%2E+IEEE+Transactions+on+Computational+Social+Systems%2C+11%282%29%3A208)
26. Changwon Kwak, Pilsu Jung, and Seonah Lee. 2023. A multimodal deep learning model using text, image, and code data for improving issue classification tasks. Applied Sciences, 13(16):9456. [find](https://scholar.google.com/scholar?q=Changwon+Kwak%2C+Pilsu+Jung%2C+and+Seonah+Lee%2E+2023%2E+A+multimodal+deep+learning+model+using+text%2C+image%2C+and+code+data+for+improving+issue+classification+tasks%2E+Applied+Sciences%2C+13%2816%29%3A9456%2E)
27. Catherine Lai, Beatrice Alex, Johanna Moore, Leimin Tian, Tatsuro Hori, and Gianpiero Francesca. 2019. Detecting topic-oriented speaker stance in conversational speech. pages 46–50. [find](https://scholar.google.com/scholar?q=Catherine+Lai%2C+Beatrice+Alex%2C+Johanna+Moore%2C+Leimin+Tian%2C+Tatsuro+Hori%2C+and+Gianpiero+Francesca%2E+2019%2E+Detecting+topic%2Doriented+speaker+stance+in+conversational+speech%2E+pages+46%E2%80%9350%2E)
28. David Lewis. 2008. Convention: A philosophical study. John Wiley & Sons. [find](https://scholar.google.com/scholar?q=David+Lewis%2E+2008%2E+Convention%3A+A+philosophical+study%2E+John+Wiley+%26+Sons%2E)
29. Chang Li, Aldo Porco, and Dan Goldwasser. 2018. Structured representation learning for online debate stance prediction. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3728–3739. [find](https://scholar.google.com/scholar?q=Chang+Li%2C+Aldo+Porco%2C+and+Dan+Goldwasser%2E+2018%2E+Structured+representation+learning+for+online+debate+stance+prediction%2E+In+Proceedings+of+the+27th+International+Conference+on+Computational+Linguistics)
30. Yupeng Li, Haorui He, Shaonan Wang, Francis C. M. Lau, and Yunya Song. 2023. Improved target-specific stance detection on social media platforms by delving into conversation threads. IEEE Transactions on Computational Social Systems, 10(6):3031–3042. [find](https://scholar.google.com/scholar?q=Yupeng+Li%2C+Haorui+He%2C+Shaonan+Wang%2C+Francis+C%2E+M%2E+Lau%2C+and+Yunya+Song%2E+2023%2E+Improved+target%2Dspecific+stance+detection+on+social+media+platforms+by+delving+into+conversation+threads%2E+IEEE+Transactions)
31. Bin Liang, Zixiao Chen, Lin Gui, Yulan He, Min Yang, and Ruifeng Xu. 2022. Zero-shot stance detection via contrastive learning. In Proceedings of the ACM Web Conference 2022, pages 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+2022%2E+Zero%2Dshot+stance+detection+via+contrastive+learning%2E+In+Proceedings+of+the+ACM+Web+Conference+2022%2C+pages+2738%E2%80%932747%2E)
32. Rui Liu, Zheng Lin, Yutong Tan, and Weiping Wang. 2021. 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+2021%2E+Enhancing+zero%2Dshot+and+few%2Dshot+stance+detection+with+commonsense+knowledge+graph%2E+In+Findings+of+the+Association+for+Computational+Linguistics) [our card](https://md.research.elamur.ai/free/papers/liu-2021-enhancing-zero-shot-few-shot/)
33. Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. Preprint, arXiv:1711.05101. [find](https://scholar.google.com/scholar?q=Ilya+Loshchilov+and+Frank+Hutter%2E+2019%2E+Decoupled+weight+decay+regularization%2E+Preprint%2C+arXiv%3A1711%2E05101%2E)
34. Veronica Lynn, Salvatore Giorgi, Niranjan Balasubramanian, and H Andrew Schwartz. 2019. Tweet classification without the tweet: An empirical examination of user versus document attributes. In Proceedings of the third workshop on natural language processing and computational social science, pages 18–… [find](https://scholar.google.com/scholar?q=Veronica+Lynn%2C+Salvatore+Giorgi%2C+Niranjan+Balasubramanian%2C+and+H+Andrew+Schwartz%2E+2019%2E+Tweet+classification+without+the+tweet%3A+An+empirical+examination+of+user+versus+document+attributes%2E+In+Proceedi)
35. Junhua Mao, Jiajing Xu, Kevin Jing, and Alan L Yuille. 2016. Training and evaluating multimodal word embeddings with large-scale web annotated images. Advances in neural information processing systems, 29. [find](https://scholar.google.com/scholar?q=Junhua+Mao%2C+Jiajing+Xu%2C+Kevin+Jing%2C+and+Alan+L+Yuille%2E+2016%2E+Training+and+evaluating+multimodal+word+embeddings+with+large%2Dscale+web+annotated+images%2E+Advances+in+neural+information+processing+systems)
36. 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)
37. Akiko Murakami and Rudy Raymond. 2010. Support or oppose? classifying positions in online debates from reply activities and opinion expressions. In Coling 2010: Posters, pages 869–875, Beijing, China. [find](https://scholar.google.com/scholar?q=Akiko+Murakami+and+Rudy+Raymond%2E+2010%2E+Support+or+oppose%3F+classifying+positions+in+online+debates+from+reply+activities+and+opinion+expressions%2E+In+Coling+2010%3A+Posters%2C+pages+869%E2%80%93875%2C+Beijing%2C+China%2E)
38. Coling 2010 Organizing Committee. [find](https://scholar.google.com/scholar?q=Coling+2010+Organizing+Committee%2E)
39. Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng. 2011. Multimodal deep learning. In Proceedings of the 28th international conference on machine learning (ICML-11), pages 689–696. [find](https://scholar.google.com/scholar?q=Jiquan+Ngiam%2C+Aditya+Khosla%2C+Mingyu+Kim%2C+Juhan+Nam%2C+Honglak+Lee%2C+and+Andrew+Y+Ng%2E+2011%2E+Multimodal+deep+learning%2E+In+Proceedings+of+the+28th+international+conference+on+machine+learning+%28ICML%2D11%29%2C+pag)
40. Ron Korenblum Pick, Vladyslav Kozhukhov, Dan Vilenchik, and Oren Tsur. 2022. Stem: Unsupervised structural embedding for stance detection. In Proceedings of the 36th AAAI Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Ron+Korenblum+Pick%2C+Vladyslav+Kozhukhov%2C+Dan+Vilenchik%2C+and+Oren+Tsur%2E+2022%2E+Stem%3A+Unsupervised+structural+embedding+for+stance+detection%2E+In+Proceedings+of+the+36th+AAAI+Conference+on+Artificial+Inte)
41. Aldo Porco and Dan Goldwasser. 2020. Predicting stance change using modular architectures. In Proceedings of the 28th International Conference on Computational Linguistics, pages 396–406. [find](https://scholar.google.com/scholar?q=Aldo+Porco+and+Dan+Goldwasser%2E+2020%2E+Predicting+stance+change+using+modular+architectures%2E+In+Proceedings+of+the+28th+International+Conference+on+Computational+Linguistics%2C+pages+396%E2%80%93406%2E)
42. Rajkumar Pujari and Dan Goldwasser. 2021. Understanding politics via contextualized discourse processing. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1353–1367, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Rajkumar+Pujari+and+Dan+Goldwasser%2E+2021%2E+Understanding+politics+via+contextualized+discourse+processing%2E+In+Proceedings+of+the+2021+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pag)
43. Dhanesh Ramachandram and Graham W Taylor. 2017. Deep multimodal learning: A survey on recent advances and trends. IEEE signal processing magazine, 34(6):96–108. [find](https://scholar.google.com/scholar?q=Dhanesh+Ramachandram+and+Graham+W+Taylor%2E+2017%2E+Deep+multimodal+learning%3A+A+survey+on+recent+advances+and+trends%2E+IEEE+signal+processing+magazine%2C+34%286%29%3A96%E2%80%93108%2E)
44. Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. [find](https://scholar.google.com/scholar?q=Aditya+Ramesh%2C+Prafulla+Dhariwal%2C+Alex+Nichol%2C+Casey+Chu%2C+and+Mark+Chen%2E+Hierarchical+text%2Dconditional+image+generation+with+clip+latents%2E) [our card](https://md.research.elamur.ai/free/papers/ramesh-2022-hierarchical-text-conditional-image-generation/)
45. Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In International Conference on Machine Learning, pages 8821–8831. PMLR. [find](https://scholar.google.com/scholar?q=Aditya+Ramesh%2C+Mikhail+Pavlov%2C+Gabriel+Goh%2C+Scott+Gray%2C+Chelsea+Voss%2C+Alec+Radford%2C+Mark+Chen%2C+and+Ilya+Sutskever%2E+2021%2E+Zero%2Dshot+text%2Dto%2Dimage+generation%2E+In+International+Conference+on+Machine+Lear) [our card](https://md.research.elamur.ai/free/papers/ramesh-2021-zero-shot-text-image-generation/)
46. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. Preprint, arXiv:1908.10084. [find](https://scholar.google.com/scholar?q=Nils+Reimers+and+Iryna+Gurevych%2E+2019%2E+Sentence%2Dbert%3A+Sentence+embeddings+using+siamese+bert%2Dnetworks%2E+Preprint%2C+arXiv%3A1908%2E10084%2E)
47. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10674– 10685. IEEE. [find](https://scholar.google.com/scholar?q=Robin+Rombach%2C+Andreas+Blattmann%2C+Dominik+Lorenz%2C+Patrick+Esser%2C+and+Bj%C3%B6rn+Ommer%2E+2022%2E+High%2Dresolution+image+synthesis+with+latent+diffusion+models%2E+In+2022+IEEE%2FCVF+Conference+on+Computer+Vision+and)
48. Younes Samih and Kareem Darwish. 2021. A few topical tweets are enough for effective user stance detection. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2637–2646. [find](https://scholar.google.com/scholar?q=Younes+Samih+and+Kareem+Darwish%2E+2021%2E+A+few+topical+tweets+are+enough+for+effective+user+stance+detection%2E+In+Proceedings+of+the+16th+Conference+of+the+European+Chapter+of+the+Association+for+Computa) [our card](https://md.research.elamur.ai/free/papers/samih-2021-few-topical-tweets-are-enough/)
49. Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. 2018. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556–2565. [find](https://scholar.google.com/scholar?q=Piyush+Sharma%2C+Nan+Ding%2C+Sebastian+Goodman%2C+and+Radu+Soricut%2E+2018%2E+Conceptual+captions%3A+A+cleaned%2C+hypernymed%2C+image+alt%2Dtext+dataset+for+automatic+image+captioning%2E+In+Proceedings+of+the+56th+Annual) [our card](https://md.research.elamur.ai/free/papers/sharma-2018-conceptual-captions-cleaned-hypernymed-image/)
50. Swapna Somasundaran and Janyce Wiebe. 2010. Recognizing stances in ideological on-line debates. In Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, pages 116–124. [find](https://scholar.google.com/scholar?q=Swapna+Somasundaran+and+Janyce+Wiebe%2E+2010%2E+Recognizing+stances+in+ideological+on%2Dline+debates%2E+In+Proceedings+of+the+NAACL+HLT+2010+workshop+on+computational+approaches+to+analysis+and+generation+of+)
51. Dhanya Sridhar, James Foulds, Bert Huang, Lise Getoor, and Marilyn Walker. 2015. Joint models of disagreement and stance in online debate. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Process… [find](https://scholar.google.com/scholar?q=Dhanya+Sridhar%2C+James+Foulds%2C+Bert+Huang%2C+Lise+Getoor%2C+and+Marilyn+Walker%2E+2015%2E+Joint+models+of+disagreement+and+stance+in+online+debate%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+)
52. Yla R Tausczik and James W Pennebaker. 2010. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology, 29(1):24–54. [find](https://scholar.google.com/scholar?q=Yla+R+Tausczik+and+James+W+Pennebaker%2E+2010%2E+The+psychological+meaning+of+words%3A+Liwc+and+computerized+text+analysis+methods%2E+Journal+of+language+and+social+psychology%2C+29%281%29%3A24%E2%80%9354%2E)
53. Marilyn Walker, Pranav Anand, Rob Abbott, and Ricky Grant. 2012a. Stance classification using dialogic properties of persuasion. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 592–596, Montréal,… [find](https://scholar.google.com/scholar?q=Marilyn+Walker%2C+Pranav+Anand%2C+Rob+Abbott%2C+and+Ricky+Grant%2E+2012a%2E+Stance+classification+using+dialogic+properties+of+persuasion%2E+In+Proceedings+of+the+2012+Conference+of+the+North+American+Chapter+of+)
54. Marilyn A Walker, Jean E Fox Tree, Pranav Anand, Rob Abbott, and Joseph King. 2012b. A corpus for research on deliberation and debate. In LREC, volume 12, pages 812–817. Istanbul, Turkey. [find](https://scholar.google.com/scholar?q=Marilyn+A+Walker%2C+Jean+E+Fox+Tree%2C+Pranav+Anand%2C+Rob+Abbott%2C+and+Joseph+King%2E+2012b%2E+A+corpus+for+research+on+deliberation+and+debate%2E+In+LREC%2C+volume+12%2C+pages+812%E2%80%93817%2E+Istanbul%2C+Turkey%2E) [our card](https://md.research.elamur.ai/free/papers/walker-2012-corpus-research-deliberation-debate/)
55. Lu Wang and Claire Cardie. 2014. Improving agreement and disagreement identification in online discussions with a socially-tuned sentiment lexicon. In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 97–106, Baltimore, Maryland.… [find](https://scholar.google.com/scholar?q=Lu+Wang+and+Claire+Cardie%2E+2014%2E+Improving+agreement+and+disagreement+identification+in+online+discussions+with+a+socially%2Dtuned+sentiment+lexicon%2E+In+Proceedings+of+the+5th+Workshop+on+Computational+)
56. Maxwell Weinzierl and Sanda Harabagiu. 2024. Tree-of-counterfactual prompting for zero-shot stance detection. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 861–880. [find](https://scholar.google.com/scholar?q=Maxwell+Weinzierl+and+Sanda+Harabagiu%2E+2024%2E+Tree%2Dof%2Dcounterfactual+prompting+for+zero%2Dshot+stance+detection%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Computational+Linguistics)
57. Haoyang Wen and Alexander G Hauptmann. 2023. Zero-shot and few-shot stance detection on varied topics via conditional generation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1491–1499. [find](https://scholar.google.com/scholar?q=Haoyang+Wen+and+Alexander+G+Hauptmann%2E+2023%2E+Zero%2Dshot+and+few%2Dshot+stance+detection+on+varied+topics+via+conditional+generation%2E+In+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Compu) [our card](https://md.research.elamur.ai/free/papers/wen-2023-zero-shot-few-shot-stance/)
58. Haoyang Wen, Eduard Hovy, and Alexander G Hauptmann. 2024. Transitive consistency constrained learning for entity-to-entity stance detection. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1467–1480. [find](https://scholar.google.com/scholar?q=Haoyang+Wen%2C+Eduard+Hovy%2C+and+Alexander+G+Hauptmann%2E+2024%2E+Transitive+consistency+constrained+learning+for+entity%2Dto%2Dentity+stance+detection%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Associati)
59. Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of human language technology conference and conference on empirical methods in natural language processing, pages 347–354. [find](https://scholar.google.com/scholar?q=Theresa+Wilson%2C+Janyce+Wiebe%2C+and+Paul+Hoffmann%2E+2005%2E+Recognizing+contextual+polarity+in+phrase%2Dlevel+sentiment+analysis%2E+In+Proceedings+of+human+language+technology+conference+and+conference+on+empi)
60. Peng Xu, Xiatian Zhu, and David A. Clifton. 2023. Multimodal learning with transformers: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):12113–12132. [find](https://scholar.google.com/scholar?q=Peng+Xu%2C+Xiatian+Zhu%2C+and+David+A%2E+Clifton%2E+2023%2E+Multimodal+learning+with+transformers%3A+A+survey%2E+IEEE+Transactions+on+Pattern+Analysis+and+Machine+Intelligence%2C+45%2810%29%3A12113%E2%80%9312132%2E)
61. Yiqun Yao and Rada Mihalcea. 2022. Modality-specific learning rates for effective multimodal additive late-fusion. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1824–1834, Dublin, Ireland. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Yiqun+Yao+and+Rada+Mihalcea%2E+2022%2E+Modality%2Dspecific+learning+rates+for+effective+multimodal+additive+late%2Dfusion%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2022%2C+pages+1824%E2%80%9318)
62. Yiqun Yao, Verónica Pérez-Rosas, Mohamed Abouele-nien, and Mihai Burzo. 2020. Morse: Multimodal sentiment analysis for real-life settings. In Proceedings of the 2020 International Conference on Multimodal Interaction, pages 387–396. [find](https://scholar.google.com/scholar?q=Yiqun+Yao%2C+Ver%C3%B3nica+P%C3%A9rez%2DRosas%2C+Mohamed+Abouele%2Dnien%2C+and+Mihai+Burzo%2E+2020%2E+Morse%3A+Multimodal+sentiment+analysis+for+real%2Dlife+settings%2E+In+Proceedings+of+the+2020+International+Conference+on+Multim)
63. Jie Yin, Nalin Narang, Paul Thomas, and Cecile Paris. 2012. Unifying local and global agreement and disagreement classification in online debates. In Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, pages 61–69, Jeju, Korea. Association for Computat… [find](https://scholar.google.com/scholar?q=Jie+Yin%2C+Nalin+Narang%2C+Paul+Thomas%2C+and+Cecile+Paris%2E+2012%2E+Unifying+local+and+global+agreement+and+disagreement+classification+in+online+debates%2E+In+Proceedings+of+the+3rd+Workshop+in+Computational+A)
64. Dongfei Yu, Jianlong Fu, Tao Mei, and Yong Rui. 2017. Multi-level attention networks for visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4709–4717. [find](https://scholar.google.com/scholar?q=Dongfei+Yu%2C+Jianlong+Fu%2C+Tao+Mei%2C+and+Yong+Rui%2E+2017%2E+Multi%2Dlevel+attention+networks+for+visual+question+answering%2E+In+Proceedings+of+the+IEEE+conference+on+computer+vision+and+pattern+recognition%2C+pa)
65. Chong Zhang, Zhenkun Zhou, Xingyu Peng, and Ke Xu. 2023. Doubleh: Twitter user stance detection via bipartite graph neural networks. Preprint, arXiv:2301.08774. — [arXiv:2301.08774](https://arxiv.org/abs/2301.08774) [find](https://scholar.google.com/scholar?q=Chong+Zhang%2C+Zhenkun+Zhou%2C+Xingyu+Peng%2C+and+Ke+Xu%2E+2023%2E+Doubleh%3A+Twitter+user+stance+detection+via+bipartite+graph+neural+networks%2E+Preprint%2C+arXiv%3A2301%2E08774%2E+%E2%80%94+%5BarXiv%3A2301%2E08774%5D%28https%3A%2F%2Farxiv%2Eorg%2F)