🧑‍🔬 Who cites Santiago Castro

88 citations of their works from 88 papers of our library (of the 141,840 whose bibliographies we read) · 25 with a page here, the most cited first · ← Santiago Castro

1. Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic AssociationAnnual Meeting of the Association for Computational Linguistics (ACL)Nan Xu, Zhixiong Zeng, Wenji Mao2020 · 206 citations
2. Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm DetectionFindings of the Association for Computational LinguisticsHongliang Pan, Zheng Lin, Peng Fu, et al.2020 · 181 citations
3. Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion AnalysisAnnual Meeting of the Association for Computational Linguistics (ACL)Dushyant Singh Chauhan, Dhanush S R, Asif Ekbal, et al.2020 · 173 citations
4. Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional NetworkAnnual Meeting of the Association for Computational Linguistics (ACL)Bin Liang, Chenwei Lou, Xiang Li, et al.2022 · 172 citations
5. Affective and Contextual Embedding for Sarcasm DetectionInternational Conference on Computational Linguistics (COLING)Nastaran Babanejad, Heidar Davoudi, Aijun An, et al.2020 · 135 citations
6. Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption ContestAnnual Meeting of the Association for Computational Linguistics (ACL)Jack Hessel, Ana Marasovic, Jena D. Hwang, et al.2023 · 107 citations
7. A Report on the 2020 Sarcasm Detection Shared TaskWorkshop on Figurative Language ProcessingDebanjan Ghosh, Avijit Vajpayee, Smaranda Muresan2020 · 92 citations
8. Towards Emotion-aided Multi-modal Dialogue Act ClassificationAnnual Meeting of the Association for Computational Linguistics (ACL)Tulika Saha, Aditya Patra, Sriparna Saha, et al.2020 · 79 citations
9. Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text DataProceedings of the First International Workshop on Natural Language Processing Beyond TextXinyu Wang, Xiaowen Sun, Tan Yang, et al.2020 · 62 citations
10. When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party DialoguesAnnual Meeting of the Association for Computational Linguistics (ACL)Shivani Kumar, Atharva Kulkarni, Md Shad Akhtar, et al.2022 · 51 citations
11. Detecting Sarcasm in Conversation Context Using Transformer-Based ModelsWorkshop on Figurative Language ProcessingAdithya Avvaru, Sanath Vobilisetty, Radhika Mamidi2020 · 46 citations
12. What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum ProbabilityFindings of the Association for Computational LinguisticsYaochen Liu, Yazhou Zhang, Qiuchi Li, et al.2021 · 43 citations
13. All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on MemesConference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)Dushyant Singh Chauhan, Dhanush S R, Asif Ekbal, et al.2020 · 38 citations
14. A Multimodal Corpus for Emotion Recognition in SarcasmLanguage Resources and Evaluation Conference (LREC)Anupama Ray, Shubham Mishra, Apoorva Nunna, et al.2022 · 35 citations
15. Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?Proceedings of the AAAI Conference on Artificial IntelligenceBen Yao, Yazhou Zhang, Qiuchi Li, et al.2024 · 33 citations
16. Multimodal Sarcasm Target Identification in TweetsAnnual Meeting of the Association for Computational Linguistics (ACL)Jiquan Wang, Lin Sun, Yi Liu, et al.2022 · 28 citations
17. Generalizable Sarcasm Detection is Just Around the Corner, of Course!Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)Hyewon Jang, Diego Frassinelli2024 · 22 citations
18. A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X PerspectivesQingcheng Zeng, An-Ran Li2022 · 14 citations
19. Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic IncongruityDebanjan Ghosh, Elena Musi, Smaranda Muresan2020 · 10 citations
20. Utilizing Weak Supervision to Create S3D: A Sarcasm Annotated DatasetWorkshop on Natural Language Processing and Computational Social Science (NLP+CSS)Jordan Painter, Helen Treharne, Diptesh Kanojia2022 · 4 citations
21. Me, myself, and ire: Effects of automatic transcription quality on emotion, sarcasm, and personality detectionWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisJohn Culnan, Seongjin Park, Meghavarshini Krishnaswamy, et al.2021 · 4 citations
22. Parallel Deep Learning-Driven Sarcasm Detection from Pop Culture Text and English Humor LiteraturearXiv:2106.05752 · In RAAI 2020. Advances in Intelligent Systems and Computing, vol 1355 (2021)Sourav Das, Anup Kumar Kolya2020 · 4 citations
23. R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasmInternational Workshop on Semantic Evaluation (SemEval)Mayukh Sharma, Ilanthenral Kandasamy, Vasantha W B2022 · 2 citations
24. Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed TechniquesLang Xiong, Raina Gao, Alyssa Jeong2025
25. Linguistic Features in Automatic Sarcasm DetectionMartina Ducret, Lauren Kruse, Carlos Martinez, et al.2020

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