Rui Wang
ABSTRACT
Existing approaches for sarcasm detection are mainly based on supervised learning, in which the promising performance largely depends on a considerable amount of labeled data or extra information. In the real world scenario, however, the abundant labeled data or extra information requires high labor cost, not to mention that sufficient annotated data is unavailable in many low-resource conditions. To alleviate this dilemma, we investigate sarcasm detection from an unsupervised perspective, in which we explore a masking and generation paradigm in the context to extract the context incongruities for learning sarcastic expression. Further, to improve the feature representations of the sentences, we use unsupervised contrastive learning to improve the sentence representation based on the standard dropout. Experimental results on six perceived sarcasm detection benchmark datasets show that our approach outperforms baselines. Simultaneously, our unsupervised method obtains comparative performance with supervised methods for the intended sarcasm dataset.
Supplemental Material
- Ameeta Agrawal and Aijun An. 2018. Affective Representations for Sarcasm Detection. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (Ann Arbor, MI, USA) (SIGIR '18). Association for Computing Machinery, New York, NY, USA, 1029--1032. https://doi.org/10.1145/3209978.3210148Google Scholar
Digital Library
- Ameeta Agrawal, Aijun An, and Manos Papagelis. 2020. Leveraging Transitions of Emotions for Sarcasm Detection. Association for Computing Machinery, New York, NY, USA, 1505--1508. https://doi.org/10.1145/3397271.3401183Google ScholarDigital Library
- David Bamman and Noah Smith. 2015. Contextualized Sarcasm Detection on Twitter. Proceedings of the International AAAI Conference onWeb and Social Media 9, 1 (2015), 574--577. https://ojs.aaai.org/index.php/ICWSM/article/view/14655 Number: 1.Google Scholar
- David Blei, Andrew Ng, and Michael Jordan. 2001. Latent Dirichlet Allocation. In Advances in Neural Information Processing Systems, T. Dietterich, S. Becker, and Z. Ghahramani (Eds.), Vol. 14. MIT Press. https://proceedings.neurips.cc/paper/2001/file/296472c9542ad4d4788d543508116cbc-Paper.pdfGoogle Scholar
- Erik Cambria, Yang Li, Frank Z. Xing, Soujanya Poria, and Kenneth Kwok. 2020. SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM '20). Association for Computing Machinery, New York, NY, USA, 105--114. https://doi.org/10.1145/3340531.3412003Google ScholarDigital Library
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In 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). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186. https://doi.org/10.18653/v1/N19--1423Google Scholar
- Kawin Ethayarajh. 2019. How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings. 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). Association for Computational Linguistics, Hong Kong, China, 55--65. https://doi.org/10.18653/v1/D19--1006Google Scholar
- Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. SimCSE: Simple Contrastive Learning of Sentence Embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 6894--6910. https://doi.org/10.18653/v1/2021.emnlp-main.552Google ScholarCross Ref
- Roberto González-Ibáñez, Smaranda Muresan, and Nina Wacholder. 2011. Identifying Sarcasm in Twitter: A Closer Look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Portland, Oregon, USA, 581--586. https://aclanthology.org/P11--2102Google Scholar
- Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, and Rada Mihalcea. 2018. CASCADE: Contextual Sarcasm Detection in Online Discussion Forums. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, Santa Fe, New Mexico, USA, 1837--1848. https://aclanthology.org/C18--1156Google Scholar
- Aditya Joshi, Samarth Agrawal, Pushpak Bhattacharyya, and Mark J. Carman. 2018. Expect the Unexpected: Harnessing Sentence Completion for Sarcasm Detection. In Computational Linguistics: 15th PACLING 2017 - Revised Selected Papers, Vol. 781. 275--287.Google Scholar
- Mikhail Khodak, Nikunj Saunshi, and Kiran Vodrahalli. 2018. A Large Self- Annotated Corpus for Sarcasm. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan. https://aclanthology.org/L18--1102Google Scholar
- 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. Association for Computational Linguistics, Online, 61--66. https://doi.org/10.18653/v1/2020.figlang-1.8Google ScholarCross Ref
- Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 7871--7880. https://doi.org/10.18653/v1/2020.acl-main.703Google ScholarCross Ref
- Bohan Li, Hao Zhou, Junxian He, MingxuanWang, Yiming Yang, and Lei Li. 2020. On the Sentence Embeddings from Pre-trained Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 9119--9130. https://doi.org/10.18653/v1/2020.emnlp-main.733Google ScholarCross Ref
- S. Lloyd. 1982. Least squares quantization in PCM. IEEE Transactions on Information Theory 28, 2 (1982), 129--137. https://doi.org/10.1109/TIT.1982.1056489Google ScholarDigital Library
- 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. ACM, Virtual Event Canada, 1844--1849. https://doi.org/10.1145/3404835.3463061Google ScholarDigital Library
- Stephanie Lukin and Marilyn Walker. 2013. Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue. In Proceedings of the Workshop on Language Analysis in Social Media. Association for Computational Linguistics, Atlanta, Georgia, 30--40. https://aclanthology.org/W13--1104Google Scholar
- Silviu Oprea and Walid Magdy. 2019. Exploring Author Context for Detecting Intended vs Perceived Sarcasm. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 2854--2859. https://doi.org/10.18653/v1/P19--1275Google ScholarCross Ref
- Silviu Oprea and Walid Magdy. 2020. iSarcasm: A Dataset of Intended Sarcasm. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 1279--1289. https://doi.org/10.18653/v1/2020.acl-main.118Google ScholarCross Ref
- Joan Plepi and Lucie Flek. 2021. Perceived and Intended Sarcasm Detection with Graph Attention Networks. In Findings of the Association for Computational Linguistics: EMNLP 2021. Association for Computational Linguistics, Punta Cana, Dominican Republic, 4746--4753. https://aclanthology.org/2021.findings-emnlp.408Google Scholar
- Tomá Ptáek, 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. Dublin City University and Association for Computational Linguistics, Dublin, Ireland, 213--223. https://aclanthology.org/C14--1022Google Scholar
- 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. Association for Computational Linguistics, Seattle, Washington, USA, 704--714. https://aclanthology.org/D13--1066Google Scholar
- Patricia Rockwell and Evelyn M. Theriot. 2001. Culture, gender, and gender mix in encoders of sarcasm: A self-assessment analysis. Communication Research Reports 18, 1 (2001), 44--52. https://doi.org/10.1080/08824090109384781 arXiv:https://doi.org/10.1080/08824090109384781Google ScholarCross Ref
- Yi Tay, Anh Tuan Luu, Siu Cheung Hui, and Jian Su. 2018. Reasoning with Sarcasm by Reading In-Between. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, 1010--1020. https://doi.org/10.18653/v1/P18--1093Google ScholarCross Ref
- Byron C. Wallace, Do Kook Choe, Laura Kertz, and Eugene Charniak. 2014. Humans Require Context to Infer Ironic Intent (so Computers Probably do, too). In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Baltimore, Maryland, 512--516. https://doi.org/10.3115/v1/P14--2084Google ScholarCross Ref
- Chuhan Wu, Fangzhao Wu, Sixing Wu, Junxin Liu, Zhigang Yuan, and Yongfeng Huang. 2018. THU_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning. In Proceedings of The 12th International Workshop on Semantic Evaluation. Association for Computational Linguistics, New Orleans, Louisiana, 51--56. https://doi.org/10.18653/v1/S18--1006.Google ScholarCross Ref
Index Terms
- Masking and Generation: An Unsupervised Method for Sarcasm Detection
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