🧑‍🔬 Who cites Diana Maynard

88 citations of their works from 88 papers of our library (of the 149,439 whose bibliographies we read) · 63 with a page here, the most cited first · ← Diana Maynard

1. SemEval-2018 Task 3: Irony Detection in English TweetsInternational Workshop on Semantic Evaluation (SemEval)Cynthia Van Hee, Els Lefever, Véronique Hoste2018 · 467 citations
2. Harnessing Context Incongruity for Sarcasm DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Aditya Joshi, Vinita Sharma, Pushpak Bhattacharyya2015 · 417 citations
3. A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural NetworksInternational Conference on Computational Linguistics (COLING)Soujanya Poria, Erik Cambria, Devamanyu Hazarika, et al.2016 · 335 citations
4. Automatic Sarcasm Detection: A SurveyarXiv:1602.03426Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman2016 · 216 citations
5. 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 · 182 citations
6. SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and ArabicIbrahim Abu Farha, Silviu Vlad Oprea, Steven Wilson, et al.2022 · 146 citations
7. Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of WordsConference on Empirical Methods in Natural Language Processing (EMNLP)Debanjan Ghosh, Weiwei Guo, Smaranda Muresan2015 · 142 citations
8. Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural NetworkAnnual Meeting of the Association for Computational Linguistics (ACL)Abhijit Mishra, Kuntal Dey, Pushpak Bhattacharyya2017 · 141 citations
9. Sarcasm Analysis Using Conversation ContextComputational LinguisticsDebanjan Ghosh, Alexander R. Fabbri, Smaranda Muresan2018 · 119 citations
10. Your Sentiment Precedes You: Using an author's historical tweets to predict sarcasmWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisAnupam Khattri, Aditya Joshi, Pushpak Bhattacharyya, et al.2015 · 115 citations
11. Offensive Language and Hate Speech Detection for DanishLanguage Resources and Evaluation Conference (LREC)Gudbjartur Ingi Sigurbergsson, Leon Derczynski2020 · 107 citations
12. Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational ApplicationsComputational LinguisticsFarah Benamara, Maite Taboada, Yannick Mathieu2017 · 104 citations
13. Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine TranslationAnnual Meeting of the Association for Computational Linguistics (ACL)Lotem Peled, Roi Reichart2017 · 95 citations
14. A Report on the 2020 Sarcasm Detection Shared TaskWorkshop on Figurative Language ProcessingDebanjan Ghosh, Avijit Vajpayee, Smaranda Muresan2020 · 92 citations
15. Harnessing Cognitive Features for Sarcasm DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Abhijit Mishra, Diptesh Kanojia, Seema Nagar, et al.2016 · 85 citations
16. Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter ConversationsACL Student Research WorkshopGavin Abercrombie, Dirk Hovy2016 · 85 citations
17. A Novel Hierarchical BERT Architecture for Sarcasm DetectionWorkshop on Figurative Language ProcessingHimani Srivastava, Vaibhav Varshney, Surabhi Kumari, et al.2020 · 62 citations
18. Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweetsCynthia Van Hee, Els Lefever, Véronique Hoste2016 · 60 citations
19. iSarcasm: A Dataset of Intended SarcasmSilviu Oprea, Walid Magdy2020 · 58 citations
20. Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm DetectionWorkshop on Figurative Language ProcessingTaha Shangipour ataei, Soroush Javdan, Behrouz Minaei-Bidgoli2020 · 56 citations
21. Exploring the Realization of Irony in Twitter DataLanguage Resources and Evaluation Conference (LREC)Cynthia Van Hee, Els Lefever, Véronique Hoste2016 · 48 citations
22. Exploring Author Context for Detecting Intended vs Perceived SarcasmAnnual Meeting of the Association for Computational Linguistics (ACL)Silviu Oprea, Walid Magdy2019 · 47 citations
23. Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuolaLanguage Resources and Evaluation Conference (LREC)Marco Stranisci, Cristina Bosco, Delia Irazú Hernández Farías, et al.2016 · 45 citations
24. We Usually Don't Like Going to the Dentist: Using Common Sense to Detect Irony on TwitterCynthia Van Hee, Els Lefever, Véronique Hoste2018 · 39 citations
25. C-Net: Contextual Network for Sarcasm DetectionWorkshop on Figurative Language ProcessingAmit Kumar Jena, Aman Sinha, Rohit Agarwal2020 · 38 citations
26. A Multimodal Corpus for Emotion Recognition in SarcasmLanguage Resources and Evaluation Conference (LREC)Anupama Ray, Shubham Mishra, Apoorva Nunna, et al.2022 · 35 citations
27. Neural Sarcasm Detection using Conversation ContextWorkshop on Figurative Language ProcessingNikhil Jaiswal2020 · 32 citations
28. ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and SarcasmInternational Workshop on Semantic Evaluation (SemEval)Delia Irazú Hernández Farías, Emilio Sulis, Viviana Patti, et al.2015 · 28 citations
29. Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Xu Guo, Boyang Li, Han Yu, et al.2021 · 27 citations
30. A Survey of Multimodal Sarcasm DetectionarXiv:2410.18882Shafkat Farabi, Tharindu Ranasinghe, Diptesh Kanojia, et al.2024 · 26 citations
31. Machine Learning-Based Model for Sentiment and Sarcasm DetectionArabic Natural Language Processing Workshop (WANLP)Hamada Nayel, Eslam Amer, Aya Allam, et al.2021 · 25 citations
32. Detecting Sarcasm is Extremely Easy ;-)Workshop on Computational Semantics beyond Events and RolesNatalie Parde, Rodney Nielsen2018 · 24 citations
33. A Survey on Automated Sarcasm Detection on TwitterarXiv:2202.02516Bleau Moores, Vijay Mago2022 · 19 citations
34. Perceived and Intended Sarcasm Detection with Graph Attention NetworksFindings of the Association for Computational LinguisticsJoan Plepi, Lucie Flek2021 · 18 citations
35. Does Commonsense help in detecting Sarcasm?Workshop on Insights from Negative Results in NLPSomnath Basu Roy Chowdhury, Snigdha Chaturvedi2021 · 17 citations
36. Ciron: a New Benchmark Dataset for Chinese Irony DetectionLanguage Resources and Evaluation Conference (LREC)Rong Xiang, Xuefeng Gao, Yunfei Long, et al.2020 · 17 citations
37. Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and ReactionAnnual Meeting of the Association for Computational Linguistics (ACL)Changrong Min, Ximing Li, Liang Yang, et al.2023 · 16 citations
38. The Effect of Sociocultural Variables on Sarcasm Communication OnlineProceedings of the ACM on Human-Computer InteractionSilviu Vlad Oprea, Walid Magdy2020 · 15 citations
39. #NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English TweetsInternational Workshop on Semantic Evaluation (SemEval)Endang Wahyu Pamungkas, Viviana Patti2018 · 14 citations
40. Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm DetectionFindings of the Association for Computational LinguisticsIbrahim Abu Farha, Steven Wilson, Silviu Oprea, et al.2022 · 13 citations
41. CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and ArabicInternational Workshop on Semantic Evaluation (SemEval)Abdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar, et al.2022 · 11 citations
42. Perceived and Intended Sarcasm Detection with Graph Attention NetworksWorkshop on Noisy User-generated TextJoan Plepi, Lucie Flek2021 · 11 citations
43. Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data AugmentationInternational Workshop on Semantic Evaluation (SemEval)Mosab Shaheen, Shubham Kumar Nigam2022 · 10 citations
44. Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic IncongruityDebanjan Ghosh, Elena Musi, Smaranda Muresan2020 · 10 citations
45. How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English LiteratureAditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, et al.2016 · 10 citations
46. Sarcasm Detection in a Disaster ContextLanguage Resources and Evaluation Conference (LREC)Tiberiu Sosea, Junyi Jessy Li, Cornelia Caragea2024 · 8 citations
47. PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended SarcasmInternational Workshop on Semantic Evaluation (SemEval)Xiyang Du, Dou Hu, Jin Zhi, et al.2022 · 7 citations
48. Irony Detection, Reasoning and Understanding in Zero-shot LearningarXiv:2501.16884Peiling Yi, Yuhan Xia, Yunfei Long2025 · 5 citations
49. Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm DetectionConference on Computational Natural Language Learning (CoNLL)Soumyadeep Jana, Animesh Dey, Ranbir Singh Sanasam2024 · 5 citations
50. “When Words Fail, Emojis Prevail”: A Novel Architecture for Generating Sarcastic Sentences With Emoji Using Valence Reversal and Semantic IncongruityAnnual Meeting of the Association for Computational Linguistics (ACL)Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, et al.2023 · 5 citations
51. Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data AugmentationarXiv:2203.04111Shubham Kumar Nigam, Mosab Shaheen2022 · 5 citations
52. Computational Sarcasm Analysis on Social Media: A Systematic ReviewarXiv:2209.06170Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, et al.2022 · 4 citations
53. TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm DetectionInternational Workshop on Semantic Evaluation (SemEval)Jason Angel, Segun Taofeek Aroyehun, Alexander Gelbukh2022 · 4 citations
54. UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detectionInternational Workshop on Semantic Evaluation (SemEval)Emmanuel Osei-Brefo, Huizhi Liang2022 · 4 citations
55. ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English TweetsInternational Workshop on Semantic Evaluation (SemEval)Kevin Swanberg, Madiha Mirza, Ted Pedersen, et al.2018 · 4 citations
56. AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning TechniquesInternational Workshop on Semantic Evaluation (SemEval)Aya Lotfy, Marwan Torki, Nagwa El-Makky2022 · 3 citations
57. Can Contextualizing User Embeddings Improve Sarcasm and Hate Speech Detection?Workshop on Natural Language Processing and Computational Social ScienceKim Breitwieser2022 · 2 citations
58. JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-gramsInternational Workshop on Semantic Evaluation (SemEval)Yaakov HaCohen-Kerner, Matan Fchima, Ilan Meyrowitsch2022 · 1 citation
59. Tackling Irony Detection using Ensemble ClassifiersLanguage Resources and Evaluation Conference (LREC)Christoph Turban, Udo Kruschwitz2022 · 1 citation
60. SarcEmp - Fine-tuning DialoGPT for Sarcasm and EmpathyProceedings of the 1st Workshop on Simulating Conversational Intelligence in Chat (SCI-CHAT 2024)Mohammed Rizwan2024
61. TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer ModelsInternational Workshop on Semantic Evaluation (SemEval)Rajalakshmi Sivanaiah, Angel Deborah S, Sakaya Milton R, et al.2022
62. Linguistic Features in Automatic Sarcasm DetectionMartina Ducret, Lauren Kruse, Carlos Martinez, et al.2020
63. #SarcasmDetection is soooo general! Towards a Domain-Independent Approach for Detecting SarcasmarXiv:1806.03369Natalie Parde, Rodney D. Nielsen2018

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