The automatic detection of figurative language, such as irony and sarcasm, is one of the most challenging tasks of Natural Language Processing (NLP). In this paper, we investigate the generalization capabilities of figurative language detection models, focusing on the case of irony and sarcasm. Firstly, we compare the most promising approaches of the state of the art. Then, we propose three different methods for reducing the generalization errors on both in- and out-domain scenarios.
Famiglini, L., Fersini, E., Rosso, P. (2021). On the Generalization of Figurative Language Detection: The Case of Irony and Sarcasm. In Natural Language Processing and Information Systems 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021, Saarbrücken, Germany, June 23–25, 2021, Proceedings (pp.178-186). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-80599-9_16].
On the Generalization of Figurative Language Detection: The Case of Irony and Sarcasm
Famiglini L.;Fersini E.
;
2021
Abstract
The automatic detection of figurative language, such as irony and sarcasm, is one of the most challenging tasks of Natural Language Processing (NLP). In this paper, we investigate the generalization capabilities of figurative language detection models, focusing on the case of irony and sarcasm. Firstly, we compare the most promising approaches of the state of the art. Then, we propose three different methods for reducing the generalization errors on both in- and out-domain scenarios.
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