Elamur Research⌕
☰
home / papers / 2023 / “When Words Fail, Emojis Prevail”: A Novel Architecture for Generating Sarcastic Sentences With Emoji Using Valence Reversal and Semantic Incongruity

“When Words Fail, Emojis Prevail”: A Novel Architecture for Generating Sarcastic Sentences With Emoji Using Valence Reversal and Semantic Incongruity

Paper · 2023 · Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir et al.

Summary

The paper tackles the problem of turning a plain, non-sarcastic English sentence into a sarcastic one that also carries a fitting emoji. Earlier generators produced text only. The authors build a three-module pipeline. First, a Reversal of Valence step uses an LSTM sentiment classifier with self-attention. Its attention weights are inverted to strip out words carrying negative sentiment, and a Positive Sentiment Induction model then adds positive wording. Second, a Retrieval of Commonsense step uses COMET DISTIL to add a context sentence that clashes with the now-positive statement, which creates semantic incongruity. Third, an Emoji Prediction module, fine-tuned from OpenAI's CLIP, picks one emoji for the result. Three human judges rated 100 outputs for sarcasticness, creativity, humor and grammaticality. The full system beat the R³ system of Chakrabarty et al. (2020) on every criterion except grammaticality. The generated corpus and the code are publicly released. The work matters because it treats sarcasm generation as multimodal, and the released data could help train sarcasm detectors.

Key points

  • The design follows Burgers et al. (2012), which lists five traits of sarcasm. The system implements two of them: valence reversal, and semantic incongruity with shared commonsense context.

  • Average human ratings were as follows:

    SystemSarcasticnessCreativityHumorGrammaticality
    Full model3.443.293.163.72
    Without context3.12.992.883.72
    Without emoji2.772.832.693.7
    R³2.322.22.14.29

  • Agreement among the three judges was high: Intraclass Correlation Coefficient values were about 0.90–0.97. R³ showed the largest rating variance.

  • The authors also tested an external check. They mixed 2k generated sarcastic sentences with 2k non-sarcastic ones and ran the Bi-GRU emoji-aware detector of Subramanian et al. (2019). It scored F1 67.28% and ROC AUC 53.33%, which the authors attribute partly to a mismatch between its training data and their sentences.

  • Stated weaknesses:

    • The learned valence reversal loses more information than R³'s rules, which hurts grammaticality.
    • Added context is often redundant for long sentences.
    • Results depend on COMET DISTIL quality.
    • Only one emoji is predicted per sentence.

Interesting ideas

  • I1. The system removes sentiment words without a lexicon: it inverts a sentiment classifier's attention and drops tokens whose weight is near the maximum, leaving a neutral skeleton to rewrite.
  • I2. Sarcasm is modelled as two separable parts: a positive surface evaluation, plus a commonsense context whose polarity contradicts it.
  • I3. The human study ablates each module (no emoji, no context) against a strong prior system, so it shows what each component adds rather than only an overall score.
  • I4. The generated sentences were checked with an independent detector and padded with an equal number of non-sarcastic sentences, so the detector faced a balanced, controlled test set.
  • I5. The authors found that rules gave better grammar but generalized poorly, while the learned model generalized but broke grammar; they propose a separate repair step afterwards.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (back): A neutral skeleton like this could serve as an intermediate state in generation, where evaluative words are re-inserted from the meaning graph with a chosen polarity.
  • I2 (frames): The irony component in prag could flag sarcasm when a frame's stated evaluation clashes with the expected polarity of the situation recorded in the text's world state.
  • I3 (learn): Per-stage ablations with fixed raters or tests would show which pipeline step actually earns its keep.
  • I4 (learn): Silver data could be validated the same way, mixing in matched negative controls and reporting AUC, while watching for distribution mismatch between training and test data.
  • I5 (back): A generate-then-repair design, with an explicit grammar-check pass over generated text, keeps each step explainable while getting broad coverage.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Islamic University of TechnologyDhaka · Bangladesh10 works · 555 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.English · 2,263 works

Cite

Cite as Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, Mohsinul Kabir, Hasan Mahmud and Md Kamrul Hasan. 2023. “When Words Fail, Emojis Prevail”: A Novel Architecture for Generating Sarcastic Sentences With Emoji Using Valence Reversal and Semantic Incongruity. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.acl-srw.47

@inproceedings{kader2023when,
  title = {{“When Words Fail, Emojis Prevail”: A Novel Architecture for Generating Sarcastic Sentences With Emoji Using Valence Reversal and Semantic Incongruity}},
  author = {Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, Mohsinul Kabir, Hasan Mahmud and Md Kamrul Hasan},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-srw.47},
  url = {https://doi.org/10.18653/v1/2023.acl-srw.47}
}

References

98 references, 49 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Marc Aguert. 2022. Paraverbal expression of verbal irony: vocal cues matter and facial cues even more. Journal of Nonverbal Behavior, 46(1):45–70. ↗
  2. Silvio Amir, Byron C Wallace, Hao Lyu, and Paula Carvalho Mário J Silva. 2016. Modelling context with user embeddings for sarcasm detection in social media. arXiv preprint arXiv:1607.00976. ↗
  3. Adithya Avvaru, Sanath Vobilisetty, and Radhika Mamidi. 2020. Detecting sarcasm in conversation context using transformer-based models. In Proceedings of the Second Workshop on Figurative Language Processing, pages 98–103.
  4. David Bamman and Noah Smith. 2015. Contextualized sarcasm detection on twitter. In proceedings of the international AAAI conference on web and social media, volume 9, pages 574–577. find
  5. Marius Cristian Buzea, Stefan Trausan-Matu, and Tra-ian Rebedea. 2022. Automatic fake news detection for romanian online news. Information, 13(3):151. ↗
  6. Yitao Cai, Huiyu Cai, and Xiaojun Wan. 2019. Multimodal sarcasm detection in twitter with hierarchical fusion model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2506–2515.
  7. John D Campbell and Albert N Katz. 2012. Are there necessary conditions for inducing a sense of sarcastic irony? Discourse Processes, 49(6):459–480. ↗
  8. Spencer Cappallo, Thomas Mensink, and Cees GM Snoek. 2015. Image2emoji: Zero-shot emoji prediction for visual media. In Proceedings of the 23rd ACM international conference on Multimedia, pages 1311–1314. find
  9. Tuhin Chakrabarty, Debanjan Ghosh, Smaranda Muresan, and Nanyun Peng. 2020. R3: Reverse, retrieve, and rank for sarcasm generation with commonsense knowledge. In Annual Meeting of the Association for Computational Linguistics. find
  10. Francesco Barbieri, Horacio Saggion, and Francesco Ronzano. 2014. Modelling sarcasm in twitter, a novel approach. In proceedings of the 5th workshop on computational approaches to subjectivity, sentiment and social media analysis, pages 50–58.
  11. Dushyant Singh Chauhan, Gopendra Vikram Singh, Aseem Arora, Asif Ekbal, and Pushpak Bhattacharyya. 2022. An emoji-aware multitask framework for multimodal sarcasm detection. Knowledge-Based Systems, 257:109924. ↗
  12. Arup Baruah, Kaushik Das, Ferdous Barbhuiya, and Kuntal Dey. 2020. Context-aware sarcasm detection using bert. In Proceedings of the Second Workshop on Figurative Language Processing, pages 83–87.
  13. Christos Baziotis, Nikos Athanasiou, Georgios Paraskevopoulos, Nikolaos Ellinas, Athanasia Kolovou, and Alexandros Potamianos. 2018. Ntua-slp at semeval-2018 task 2: Predicting emojis using rnns with context-aware attention. arXiv preprint arXiv:1804.06657. ↗
  14. Santosh Kumar Bharti, Korra Sathya Babu, and Sanjay Kumar Jena. 2015. Parsing-based sarcasm sentiment recognition in twitter data. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 1373– 1380. IEEE. find
  15. Santosh Kumar Bharti, Ramkrushna Pradhan, Korra Sathya Babu, and Sanjay Kumar Jena. 2017. Sarcasm analysis on twitter data using machine learning approaches. Trends in Social Network Analysis, pages 51–76. ↗
  16. Santosh Kumar Bharti, Bakhtyar Vachha, RK Pradhan, Korra Sathya Babu, and Sanjay Kumar Jena. 2016. Sarcastic sentiment detection in tweets streamed in real time: a big data approach. Digital Communications and Networks, 2(3):108–121. ↗
  17. Tanvi Dadu and Kartikey Pant. 2020. Sarcasm detection using context separators in online discourse. In Proceedings of the Second Workshop on Figurative Language Processing, pages 51–55. ↗
  18. Sourav Das, Soumitra Ghosh, Anup Kumar Kolya, and Asif Ekbal. 2022. Unparalleled sarcasm: a framework of parallel deep lstms with cross activation functions towards detection and generation of sarcastic statements. Language Resources and Evaluation, pages 1–38. ↗
  19. Dmitry Davidov, Oren Tsur, and Ari Rappoport. 2010. Semi-supervised recognition of sarcasm in twitter and amazon. In Proceedings of the fourteenth conference on computational natural language learning, pages 107–116.
  20. Yufeng Diao, Hongfei Lin, Liang Yang, Xiaochao Fan, Yonghe Chu, Kan Xu, and Di Wu. 2020. A multi-dimension question answering network for sarcasm detection. IEEE Access, 8:135152–135161. ↗
  21. Andrea Bowes and Albert Katz. 2011. When sarcasm stings. Discourse Processes, 48(4):215–236. ↗
  22. Ning Ding, Sheng-wei Tian, and Long Yu. 2022. A multimodal fusion method for sarcasm detection based on late fusion. Multimedia Tools and Applications, 81(6):8597–8616. ↗
  23. Christian Burgers, Margot Van Mulken, and Peter Jan Schellens. 2012. Verbal irony: Differences in usage across written genres. Journal of Language and Social Psychology, 31(3):290–310. find
  24. Xiangjue Dong, Changmao Li, and Jinho D Choi. 2020. Transformer-based context-aware sarcasm detection in conversation threads from social media. arXiv preprint arXiv:2005.11424. ↗
  25. Megan L Dress, Roger J Kreuz, Kristen E Link, and Gina M Caucci. 2008. Regional variation in the use of sarcasm. Journal of Language and Social Psychology, 27(1):71–85. ↗
  26. Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531. ↗
  27. Ibrahim Abu Farha and Walid Magdy. 2020. From arabic sentiment analysis to sarcasm detection: The arsarcasm dataset. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pages 32–39.
  28. Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rah-wan, and Sune Lehmann. 2017. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. arXiv preprint arXiv:1708.00524. ↗
  29. Elena Filatova. 2012. Irony and sarcasm: Corpus generation and analysis using crowdsourcing. In Lrec, pages 392–398. Citeseer.
  30. Suzana Ilić, Edison Marrese-Taylor, Jorge A Balazs, and Yutaka Matsuo. 2018. Deep contextualized word representations for detecting sarcasm and irony. arXiv preprint arXiv:1809.09795. ↗
  31. Tanya Jain, Nilesh Agrawal, Garima Goyal, and Niyati Aggrawal. 2017. Sarcasm detection of tweets: A comparative study. In 2017 Tenth International Conference on Contemporary Computing (IC3), pages 1–6. IEEE. find
  32. Nikhil Jaiswal. 2020. Neural sarcasm detection using conversation context. In Proceedings of the Second Workshop on Figurative Language Processing, pages 77–82.
  33. Richard J Gerrig and Raymond W Gibbs Jr. 1988. Beyond the lexicon: Creativity in language production. Metaphor and Symbol, 3(3):1–19. ↗
  34. Soroush Javdan, Behrouz Minaei-Bidgoli, et al. 2020. Applying transformers and aspect-based sentiment analysis approaches on sarcasm detection. In Proceedings of the second workshop on figurative language processing, pages 67–71.
  35. Richard J Gerrig and Yevgeniya Goldvarg. 2000. Additive effects in the perception of sarcasm: Situational disparity and echoic mention. Metaphor and Symbol, 15(4):197–208. ↗
  36. Amit Kumar Jena, Aman Sinha, and Rohit Agarwal. 2020. C-net: Contextual network for sarcasm detection. In Proceedings of the second workshop on figurative language processing, pages 61–66.
  37. Aniruddha Ghosh and Tony Veale. 2016. Fracking sarcasm using neural network. In Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis, pages 161–169. ↗
  38. Aditya Joshi, Diptesh Kanojia, Pushpak Bhattacharyya, and Mark Carman. 2017. Sarcasm suite: a browser-based engine for sarcasm detection and generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31. find
  39. Aniruddha Ghosh and Tony Veale. 2017. Magnets for sarcasm: Making sarcasm detection timely, contextual and very personal. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 482–491. ↗
  40. Debanjan Ghosh, Alexander R Fabbri, and Smaranda Muresan. 2018. Sarcasm analysis using conversation context. Computational Linguistics, 44(4):755–792. ↗
  41. Aditya Joshi, Anoop Kunchukuttan, Pushpak Bhattacharyya, and Mark James Carman. 2015. Sarcasmbot: An open-source sarcasm-generation module for chatbots. In WISDOM Workshop at KDD. find
  42. Aditya Joshi, Vaibhav Tripathi, Kevin Patel, Pushpak Bhattacharyya, and Mark Carman. 2016. Are word embedding-based features useful for sarcasm detection? arXiv preprint arXiv:1610.00883. ↗
  43. A Kalaivani and D Thenmozhi. 2020. Sarcasm identification and detection in conversion context using bert. In Proceedings of the Second Workshop on Figurative Language Processing, pages 72–76.
  44. Hunter Gregory, Steven Li, Pouya Mohammadi, Natalie Tarn, Rachel Draelos, and Cynthia Rudin. 2020. A transformer approach to contextual sarcasm detection in twitter. In Proceedings of the second workshop on figurative language processing, pages 270–275.
  45. Raj Kumar Gupta and Yinping Yang. 2017. Crystalnest at semeval-2017 task 4: Using sarcasm detection for enhancing sentiment classification and quantification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 626– 633. find
  46. Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, and Rada Mihalcea. 2018. Cascade: Contextual sarcasm detection in online discussion forums. arXiv preprint arXiv:1805.06413. ↗
  47. Mikhail Khodak, Nikunj Saunshi, and Kiran Vodrahalli. 2017. A large self-annotated corpus for sarcasm. arXiv preprint arXiv:1704.05579. ↗
  48. Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M Rush. 2017. Opennmt: Open-source toolkit for neural machine translation. arXiv preprint arXiv:1701.02810. ↗
  49. Akshi Kumar, Saurabh Raj Sangwan, Anshika Arora, Anand Nayyar, Mohamed Abdel-Basset, et al. 2019. Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE access, 7:23319–23328. find
  50. Amardeep Kumar and Vivek Anand. 2020. Transformers on sarcasm detection with context. In Proceedings of the second workshop on figurative language processing, pages 88–92.
  51. Avinash Kumar, Vishnu Teja Narapareddy, Pranjal Gupta, Veerubhotla Aditya Srikanth, Lalita Bhanu Murthy Neti, and Aruna Malapati. 2021. Adversarial and auxiliary features-aware bert for sarcasm detection. In 8th ACM IKDD CODS and 26th COMAD, pages 163–170. find
  52. Avinash Kumar, Vishnu Teja Narapareddy, Veerubhotla Aditya Srikanth, Aruna Malapati, and Lalita Bhanu Murthy Neti. 2020. Sarcasm detection using multi-head attention based bidirectional lstm. Ieee Access, 8:6388–6397. ↗
  53. Martin D Lampert and Susan M Ervin-Tripp. 2006. Risky laughter: Teasing and self-directed joking among male and female friends. Journal of Pragmatics, 38(1):51–72. ↗
  54. Hankyol Lee, Youngjae Yu, and Gunhee Kim. 2020. Augmenting data for sarcasm detection with unlabeled conversation context. arXiv preprint arXiv:2006.06259. ↗
  55. John S Leggitt and Raymond W Gibbs. 2000. Emotional reactions to verbal irony. Discourse processes, 29(1):1–24. ↗
  56. Bin Liang, Chenwei Lou, Xiang Li, Min Yang, Lin Gui, Yulan He, Wenjie Pei, and Ruifeng Xu. 2022. Multimodal sarcasm detection via cross-modal graph convolutional network. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1767– 1777.
  57. Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2019. Commongen: A constrained text generation challenge for generative commonsense reasoning. arXiv preprint arXiv:1911.03705. ↗
  58. 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. ↗
  59. Edward Loper and Steven Bird. 2002. Nltk: The natural language toolkit. arXiv preprint cs/0205028. find
  60. Chenwei Lou, Bin Liang, Lin Gui, Yulan He, Yixue Dang, and Ruifeng Xu. 2021. Affective dependency graph for sarcasm detection. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1844–1849. find
  61. Navonil Majumder, Soujanya Poria, Haiyun Peng, Niyati Chhaya, Erik Cambria, and Alexander Gelbukh. 2019. Sentiment and sarcasm classification with multitask learning. IEEE Intelligent Systems, 34(3):38– 43. ↗
  62. Aaron Maladry, Els Lefever, Cynthia Van Hee, and Veronique Hoste. 2022. Irony detection for dutch: a venture into the implicit. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 172–181. find
  63. Diana G Maynard and Mark A Greenwood. 2014. Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In Lrec 2014 proceedings. ELRA.
  64. Abhijit Mishra, Tarun Tater, and Karthik Sankaranarayanan. 2019. A modular architecture for unsupervised sarcasm generation. 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 6144–6154.
  65. Abdullah Y Muaad, Hanumanthappa Jayappa Davan-agere, JV Benifa, Amerah Alabrah, Mufeed Ahmed Naji Saif, D Pushpa, Mugahed A Al-Antari, and Taha M Alfakih. 2022. Artificial intelligence-based approach for misogyny and sarcasm detection from arabic texts. ↗
  66. Computational Intelligence and Neuroscience, 2022. find
  67. Shubhadeep Mukherjee and Pradip Kumar Bala. 2017. Detecting sarcasm in customer tweets: an nlp based approach. Industrial Management & Data Systems. ↗
  68. Usman Naseem, Imran Razzak, Peter Eklund, and Katarzyna Musial. 2020. Towards improved deep contextual embedding for the identification of irony and sarcasm. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–7. IEEE. find
  69. Silviu Oprea, Steven Wilson, and Walid Magdy. 2021. Chandler: An explainable sarcastic response generator. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 339–349.
  70. Shereen Oraby, Vrindavan Harrison, Amita Misra, Ellen Riloff, and Marilyn Walker. 2017. Are you serious?: Rhetorical questions and sarcasm in social media dialog. arXiv preprint arXiv:1709.05305. ↗
  71. Reynier Ortega-Bueno, Paolo Rosso, and José E Medina Pagola. 2022. Multi-view informed attention-based model for irony and satire detection in spanish variants. Knowledge-Based Systems, 235:107597. ↗
  72. Rajnish Pandey and Jyoti Prakash Singh. 2023. Bert-lstm model for sarcasm detection in code-mixed social media post. Journal of Intelligent Information Systems, 60(1):235–254. ↗
  73. Soujanya Poria, Erik Cambria, Devamanyu Hazarika, and Prateek Vij. 2016. A deeper look into sarcastic tweets using deep convolutional neural networks. arXiv preprint arXiv:1610.08815. ↗
  74. Rolandos-Alexandros Potamias, Georgios Siolas, and Andreas Stafylopatis. 2019. A robust deep ensemble classifier for figurative language detection. In International Conference on Engineering Applications of Neural Networks, pages 164–175. Springer. find
  75. Rolandos Alexandros Potamias, Georgios Siolas, and Andreas-Georgios Stafylopatis. 2020. A transformer-based approach to irony and sarcasm detection. Neural Computing and Applications, 32(23):17309– 17320. ↗
  76. Anukarsh G Prasad, S Sanjana, Skanda M Bhat, and BS Harish. 2017. Sentiment analysis for sarcasm detection on streaming short text data. In 2017 2nd International Conference on Knowledge Engineering and Applications (ICKEA), pages 1–5. IEEE. find
  77. Tomáš Ptáček, Ivan Habernal, and Jun Hong. 2014. Sarcasm detection on czech and english twitter. In Proceedings of COLING 2014, the 25th international conference on computational linguistics: Technical papers, pages 213–223.
  78. Veranika Puhacheuskaya and Juhani Järvikivi. 2022. I was being sarcastic!: The effect of foreign accent and political ideology on irony (mis) understanding. Acta Psychologica, 222:103479. ↗
  79. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR. ↗
  80. Yafeng Ren, Donghong Ji, and Han Ren. 2018. Context-augmented convolutional neural networks for twitter sarcasm detection. Neurocomputing, 308:1–7. ↗
  81. Stephen Skalicky and Scott Crossley. 2018. Linguistic features of sarcasm and metaphor production quality. In Proceedings of the Workshop on Figurative Language Processing, pages 7–16.
  82. Himani Srivastava, Vaibhav Varshney, Surabhi Kumari, and Saurabh Srivastava. 2020. A novel hierarchical bert architecture for sarcasm detection. In Proceedings of the Second Workshop on Figurative Language Processing, pages 93–97.
  83. Jayashree Subramanian, Varun Sridharan, Kai Shu, and Huan Liu. 2019. Exploiting emojis for sarcasm detection. In International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation, pages 70–80. Springer. find
  84. Yi Tay, Luu Anh Tuan, Siu Cheung Hui, and Jian Su. 2018. Reasoning with sarcasm by reading in-between. arXiv preprint arXiv:1805.02856. ↗
  85. Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2018. Semeval-2018 task 3: Irony detection in english tweets. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 39– 50.
  86. Zhiyuan Wen, Lin Gui, Qianlong Wang, Mingyue Guo, Xiaoqi Yu, Jiachen Du, and Ruifeng Xu. 2022. Sememe knowledge and auxiliary information enhanced approach for sarcasm detection. Information Processing & Management, 59(3):102883. ↗
  87. Peter West, Chandra Bhagavatula, Jack Hessel, Jena D Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, and Yejin Choi. 2021. Symbolic knowledge distillation: from general language models to commonsense models. arXiv preprint arXiv:2110.07178. ↗
  88. Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, and Ruihong Huang. 2013. Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 704–714.
  89. Adina Williams, Nikita Nangia, and Samuel R Bowman. 2017. A broad-coverage challenge corpus for sentence understanding through inference. arXiv preprint arXiv:1704.05426. ↗
  90. Jie Ruan, Yue Wu, Xiaojun Wan, and Yuesheng Zhu. 2022. How to describe images in a more funny way? towards a modular approach to cross-modal sarcasm generation. arXiv preprint arXiv:2211.10992. ↗
  91. Chuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Junxin Liu, and Yongfeng Huang. 2018. Thu_ngn at semeval-2018 task 2: Residual cnn-lstm network with attention for english emoji prediction. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 410–414.
  92. Edoardo Savini and Cornelia Caragea. 2022. Intermediate-task transfer learning with bert for sarcasm detection. Mathematics, 10(5):844. ↗
  93. Rossano Schifanella, Paloma De Juan, Joel Tetreault, and Liangliang Cao. 2016. Detecting sarcasm in multimodal social platforms. In Proceedings of the 24th ACM international conference on Multimedia, pages 1136–1145. ↗
  94. Boaz Shmueli, Lun-Wei Ku, and Soumya Ray. 2020. Reactive supervision: A new method for collecting sarcasm data. arXiv preprint arXiv:2009.13080. ↗
  95. Deirdre Wilson. 2006. The pragmatics of verbal irony: Echo or pretence? Lingua, 116(10):1722–1743. ↗
  96. Jingjing Xu, Xu Sun, Qi Zeng, Xuancheng Ren, Xiaodong Zhang, Houfeng Wang, and Wenjie Li. 2018. Unpaired sentiment-to-sentiment translation: A cycled reinforcement learning approach. arXiv preprint arXiv:1805.05181. ↗
  97. Meishan Zhang, Yue Zhang, and Guohong Fu. 2016. Tweet sarcasm detection using deep neural network. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: technical papers, pages 2449–2460.
  98. Yazhou Zhang, Dan Ma, Prayag Tiwari, Chen Zhang, Mehedi Masud, Mohammad Shorfuzzaman, and Dawei Song. 2023. Stance-level sarcasm detection with bert and stance-centered graph attention networks. ACM Transactions on Internet Technology, 23(2):1–21. ↗

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/kader-2023-when-words-fail-emojis-prevail/