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13 - Great Expectations and EPIC Fails: A Computational Perspective on Irony and Sarcasm

from Part IV - Irony in Linguistic Communication

Published online by Cambridge University Press:  20 December 2023

Herbert L. Colston
Affiliation:
University of Alberta
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Summary

We typically believe that irony is a completely human affair, but there have been interesting attempts to create computational models of irony use and understanding. This chapter presents an overview of some of these models, especially as implemented as conversational agents. One of the beauties, and major challenges, of computer modeling is that it forces researchers to make concrete decisions on how best to implement some linguistic observation or theoretical idea (e.g., how to create a workable model of echoic mention, pretense, or what is meant by incongruity). Veale presents his EPIC model in which an expectation (E) predicts a property (P) of an instance (I) of concept (C) that can get upended by an ironic utterance. This model provides a quantifiable view of what it means for an ironic utterance to achieve its desired effect on an audience. The success of an ironic utterance hinges on its capacity to highlight the failure of a reasonable expectation. The effectiveness of this computational model was partly assessed by obtaining human judgments about the meaning and quality of different ironic utterances in varying contexts that are suggestive of different expectations. In this way, Veale’s work offers insights as to how engineering solutions may be very informative about the way irony functions in human communication.

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Publisher: Cambridge University Press
Print publication year: 2023

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References

Attardo, S. (2009). Register-based humor. In Raskin, V. (Ed.), The Primer of Humor Research (pp. 230253). De Gruyter Mouton.Google Scholar
Attardo, S., Hempelmann, C. F., & Di Maio, S. (2002). Script oppositions and logical mechanisms: Modeling incongruities and their resolutions. Humor: International Journal of Humor Research, 15(1), 346.Google Scholar
Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., & Choi, Y. (2019). COMET: Common-sense transformers for automatic knowledge graph construction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 4762–4779). Florence.Google Scholar
Chandler, D. (2020). Semiotics: The basics. Routledge.Google Scholar
Chen, M. X., Lee, B. N., & Bansal, G. (2019). Gmail smart compose: Real-time assisted writing. Proceedings of the 25th ACM SIGKDD international conference on Knowledge Discovery & Data Mining (pp. 22872295).CrossRefGoogle Scholar
Clark, H. H., & Gerrig, R. J., (1984). On the pretense theory of irony. Journal of Experimental Psychology: General, 113(1), 121126.CrossRefGoogle ScholarPubMed
Garmendia, J. (2018). Irony as opposition. In Irony (pp. 1741). Cambridge University Press.CrossRefGoogle Scholar
Gentner, D. (1983). Structure-mapping: A theoretical framework. Cognitive Science, 7(2), 155170.Google Scholar
Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Barnden, J., & Reyes, A., (2015). Semeval-2015 task 11: Sentiment analysis of figurative language in twitter. Proceedings of the 9th international workshop on semantic evaluation (pp. 470–478). Denver, Colorado.Google Scholar
Ghosh, A., & Veale, T. (2016). Fracking sarcasm with neural networks. In Balahur, A., van der Goot, E., Vossen, P., & Montoyo, A. (Eds.), Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 161168). San Diego, California.Google Scholar
Ghosh, A, & Veale, T. (2017).Magnets for sarcasm: Making sarcasm detection timely, contextual and very personal. In Palmer, M., Hwa, R., & Riedel, S. (Eds.), Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 493502). Copenhagen, Denmark.Google Scholar
Giora, R. (2018). Lying, irony, and default interpretation. In Meibauer, J. (Ed.), The Oxford handbook of lying. Oxford University Press.Google Scholar
Giora, R., Ofer Fein, A. K., Elnatan, I., Shuval, N., & Zur, A. (2004). Weapons of mass distraction: Optimal innovation and pleasure ratings. Metaphor and Symbol, 19(2), 115141.Google Scholar
Grice, H. P. (1975). Logic and conversation. In Cole, P. & Morgan, J. L. (Eds.), Syntax and semantics 3: Speech acts (pp. 183198). Academic Press.Google Scholar
Hanks, P. (2013). Lexical analysis: Norms and exploitations. MIT Press.Google Scholar
Hao, Y., & Veale, T. (2010). An ironic fist in a velvet glove: Creative mis-representation in the construction of ironic similes. Minds and Machines, 20(4), 483488.CrossRefGoogle Scholar
Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. In Boitet, C. (Ed.), Proceedings of the 15th International Conference on Computational Linguistics (pp. 539545). Nantes, France,Google Scholar
Kreuz, R. J., & Glucksberg, S. (1989). How to be sarcastic: The echoic reminder theory of verbal irony. Journal of Experimental Psychology: General, 118(4), 374386.Google Scholar
Kumon-Nakamura, S., Glucksberg, S., & Brown, T. (1995). How about another piece of pie: The allusional pretense theory of discourse irony. Journal of Experimental Psychology: General, 124(1), 321.Google Scholar
Naseem, U., Razzak, I., Eklund, P., & Musial, K. (2020). Towards improved deep contextual embedding for the identification of irony and sarcasm. Proceedings of the International Joint Conference on Neural Networks (IJCNN) (pp. 17). Glasgow, UK.Google Scholar
Orwell, G. (1946, April). Politics and the English language. Horizon, 13(76), 252265.Google Scholar
Potamias, R. A., Siolas, G., & Stafylopatis, A. G. (2020). A transformer-based approach to irony and sarcasm detection. Neural Computing and Applications, 32, 1730917320.Google Scholar
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskeve, I. (2019). Language models are unsupervised multitask learners. Open AI Technical Paper.Google Scholar
Raskin, V. (1984). Semantic mechanisms of humor. D. Reidel.Google Scholar
Reyes, A., Rosso, P., & Veale, T. (2013). A multidimensional approach for detecting irony in Twitter. Language Resources & Evaluation, 47, 239268.CrossRefGoogle Scholar
Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., & Huang, R. (2013). Sarcasm as contrast between a positive sentiment and negative situation. In Yarowsky, D., Baldwin, T., Korhonen, A, Livescu, K., & Bethard, S. (Eds.), Proceedings of the conference on empirical methods in natural language processing (pp. 704714). Seattle, Washington.Google Scholar
Searle, J. (1980). Minds, brains and programs. Behavioral and Brain Sciences, 3(3), 417457.Google Scholar
Shahaf, D., Horvitz, E., & Mankoff, R. (2015). Inside jokes: Identifying humorous cartoon captions. In Joachims, T., Webb, G., Margineantu, D., & Williams, G. (Eds.), Proceedings of the 21st ACM SIGKDD conference on knowledge discovery and data mining (pp. 10651074). Sydney, Australia.CrossRefGoogle Scholar
Sperber, D. (1984). Verbal irony: Pretense or echoic mention? Journal of Experimental Psychology: General, 133(1), 130136.Google Scholar
Sperber, D., & Wilson, D. (1981). Irony and the use-mention distinction. In Cole, P. (Ed.), Radical pragmatics (pp. 295318). Academic Press.Google Scholar
Tausczik, Y. R., & Pennebaker, J. W. (2009). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 2454.Google Scholar
Tsur, O., Davidov, D., & Rappoport, A. (2010). ICWSM – A great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. In Cohen, W. & Gosling, S. (Eds.), Proceedings of the 4th international AAAI conference on weblogs and social media (pp. 161169). Washington, DC.Google Scholar
Valitutti, A., & Veale, T. (2015). Inducing an ironic effect in automated tweets. In Schuller, B. (Ed.), Proceedings of the international conference on affective computing and intelligent interaction (ACII) (pp. 153159). Xi’an, China.Google Scholar
Veale, T. (2013). Humorous similes. Humor: The International Journal of Humor Research, 21(1), 321.Google Scholar
Veale, T. (2018). The “default” in our stars: Signposting nondefaultness in ironic discourse. Metaphor and Symbol, 33(3), 175184.Google Scholar
Veale, T., & Valitutti, A. (2017). Sparks will fly: Engineering creative script conflicts. Connection Science, 29(4), 332349.Google Scholar
Whissell, C. (1989). The dictionary of affect in language. In Plutchik, R. & Kellerman, H. (Ed.), Emotion: Theory and research (pp. 113131). Harcourt Brace.Google Scholar
Zhang, S., Zhang, X., Chan, J., & Rosso, P. (2018). Irony detection via sentiment-based transfer learning. Journal of Information Processing and Management, 56(5), 16331644.CrossRefGoogle Scholar

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