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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Notes
- 1.
Sarcasm task: batch size 64, learning rate 0.0001, optimizer AdamW and 80 epochs. Irony task: batch size 32, learning rate 0.00002, optimizer AdamW, and 100 epochs.
- 2.
Sarcasm task: batch size 32, learning rate 0.00001, optimizer Adam and 25 epochs. Irony task: batch size 32, learning rate 0.0002, optimizer Adam, and 35 epochs.
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Famiglini, L., Fersini, E., Rosso, P. (2021). On the Generalization of Figurative Language Detection: The Case of Irony and Sarcasm. In: Métais, E., Meziane, F., Horacek, H., Kapetanios, E. (eds) Natural Language Processing and Information Systems. NLDB 2021. Lecture Notes in Computer Science(), vol 12801. Springer, Cham. https://doi.org/10.1007/978-3-030-80599-9_16
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