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# Who cites «Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue»

56 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/oraby-2016-creating-characterizing-diverse-corpus-sarcasm/)

1. [A Corpus of Misunderstood Irony on Turkish Social Media](https://aclanthology.org/2026.lrec-1.879/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
2. [A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detection](https://aclanthology.org/2026.wassa-1.21/) *— 2026 · The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026)*
3. [Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering](https://aclanthology.org/2026.acl-long.193/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [LLMs in Sarcasm Detection? It's elementary! (Or is it?)](https://aclanthology.org/2026.acl-long.1346/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [On Emergent Social World Models –- Evidence for Functional Integration of Theory of Mind and Pragmatic Reasoning in Language Models](https://aclanthology.org/2026.acl-long.1735/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection](https://aclanthology.org/2026.acl-long.64/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [SarcasTürk: Turkish Context-Aware Sarcasm Detection Dataset](https://aclanthology.org/2026.sigturk-1.6/) *— 2026 · Proceedings of the Second Workshop Natural Language Processing for Turkic Languages (SIGTURK 2026)*
8. [Class Distillation with Mahalanobis Contrast: An Efficient Training Paradigm for Pragmatic Language Understanding Tasks](https://aclanthology.org/2025.acl-long.1424/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning](https://aclanthology.org/2025.coling-main.608/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
10. [Irony Detection, Reasoning and Understanding in Zero-shot Learning](https://arxiv.org/abs/2501.16884) *— 2025 · arXiv:2501.16884*
11. [Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges](https://aclanthology.org/2025.acl-long.425/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://aclanthology.org/2025.findings-emnlp.1308/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
13. [Rhetorical Device-Aware Sarcasm Detection with Counterfactual Data Augmentation](https://aclanthology.org/2025.findings-acl.607/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
14. [Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning](https://aclanthology.org/2025.findings-emnlp.570/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
15. [The Difficult Case of Intended and Perceived Sarcasm: a Challenge for Humans and Large Language Models](https://aclanthology.org/2025.iwcs-main.23/) *— 2025 · Proceedings of the 16th International Conference on Computational Semantics*
16. [Generalizable Sarcasm Detection is Just Around the Corner, of Course!](https://aclanthology.org/2024.naacl-long.238/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
17. [Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?](https://arxiv.org/abs/2407.12725) *— 2024 · arXiv:2407.12725*
18. [KoCoSa: Korean Context-aware Sarcasm Detection Dataset](https://aclanthology.org/2024.lrec-main.864/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
19. [Sarcasm Detection in a Disaster Context](https://aclanthology.org/2024.lrec-main.1247/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
20. [SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding](https://arxiv.org/abs/2408.11319) *— 2024 · arXiv:2408.11319*
21. [Was that Sarcasm?: A Literature Survey on Sarcasm Detection](https://arxiv.org/abs/2412.00425) *— 2024 · arXiv:2412.00425*
22. [When Do ``More Contexts'' Help with Sarcasm Recognition?](https://aclanthology.org/2024.lrec-main.1525/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
23. [Contrastive Learning of Sociopragmatic Meaning in Social Media](https://aclanthology.org/2023.findings-acl.152/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
24. [Figurative Language Processing: A Linguistically Informed Feature Analysis of the Behavior of Language Models and Humans](https://aclanthology.org/2023.findings-acl.622/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
25. [Prompt Tuning with Contradictory Intentions for Sarcasm Recognition](https://aclanthology.org/2023.eacl-main.25/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
26. [The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages](https://aclanthology.org/2023.emnlp-main.160/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
27. [A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://aclanthology.org/2022.findings-naacl.126/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
28. [A Multimodal Corpus for Emotion Recognition in Sarcasm](https://aclanthology.org/2022.lrec-1.756/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
29. [A Pragmatics-Centered Evaluation Framework for Natural Language Understanding](https://aclanthology.org/2022.lrec-1.255/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
30. [A Survey on Automated Sarcasm Detection on Twitter](https://arxiv.org/abs/2202.02516) *— 2022 · arXiv:2202.02516*
31. [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://arxiv.org/abs/2209.06170) *— 2022 · arXiv:2209.06170*
32. [Explainable Detection of Sarcasm in Social Media](https://aclanthology.org/2021.wassa-1.4/) *— 2021 · Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis*
33. [Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection](https://aclanthology.org/2021.naacl-main.425/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
34. [Parallel Deep Learning-Driven Sarcasm Detection from Pop Culture Text and English Humor Literature](https://arxiv.org/abs/2106.05752) *— 2021 · arXiv:2106.05752 · In RAAI 2020. Advances in Intelligent Systems and Computing, vol 1355 (2021)*
35. [``Laughing at you or with you'''': The Role of Sarcasm in Shaping the Disagreement Space](https://aclanthology.org/2021.eacl-main.171/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
36. [A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks](https://aclanthology.org/2020.acl-main.514/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
37. [Affective and Contextual Embedding for Sarcasm Detection](https://aclanthology.org/2020.coling-main.20/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
38. [Exploring the Role of Context to Distinguish Rhetorical and Information-Seeking Questions](https://aclanthology.org/2020.acl-srw.41/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop*
39. [The Design and Construction of a Chinese Sarcasm Dataset](https://aclanthology.org/2020.lrec-1.619/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
40. [An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models](https://aclanthology.org/N19-1213/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
41. [Attention-based Conditioning Methods for External Knowledge Integration](https://aclanthology.org/P19-1385/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
42. [Cross-Cultural Transfer Learning for Text Classification](https://aclanthology.org/D19-1400/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
43. [Wait Signals Predict Sarcasm in Online Debates](https://aclanthology.org/2019.dnd-10.2/) *— 2019*
44. [A Large Self-Annotated Corpus for Sarcasm](https://aclanthology.org/L18-1102/) *— 2018 · Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)*
45. [Deep contextualized word representations for detecting sarcasm and irony](https://aclanthology.org/W18-6202/) *— 2018 · Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis*
46. [Sarcasm Analysis Using Conversation Context](https://aclanthology.org/J18-4009/) *— 2018*
47. [Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog](https://md.mova.elamur.ai/free/en/docs/papers/oraby-2017-are-you-serious-rhetorical-questions/) *— our card · 2017 · Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue*
48. [Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion](https://aclanthology.org/E17-1070/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers*
49. [Finding Good Conversations Online: The Yahoo News Annotated Comments Corpus](https://aclanthology.org/W17-0802/) *— 2017 · Proceedings of the 11th Linguistic Annotation Workshop*
50. [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://aclanthology.org/D17-1169/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
51. bourgeade-2021-plongements-interpretables-pour-la-detection
52. bowden-2025-dialogue-scaffolding-producing-realistic-corpus
53. farzam-2024-multi-task-learning-improves-performance
54. katsios-2024-figuratively-speaking-authorship-attribution-multi
55. song-2025-emotion-o1-adaptive-long-reasoning
56. winkler-2026-indirect-question-answering-english-german
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