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You Can’t Learn What’s Not There: Self Supervised Learning and the Poverty of the Stimulus Read chapter Read first chapter

2021 | OriginalPaper | Chapter

On the Generalization of Figurative Language Detection: The Case of Irony and Sarcasm

Authors : Lorenzo Famiglini, Elisabetta Fersini, Paolo Rosso

Published in: Natural Language Processing and Information Systems

Publisher: Springer International Publishing

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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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Footnotes
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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Metadata
Title
On the Generalization of Figurative Language Detection: The Case of Irony and Sarcasm
Authors
Lorenzo Famiglini
Elisabetta Fersini
Paolo Rosso
Copyright Year
2021
Book
Natural Language Processing and Information Systems

Print ISBN: 978-3-030-80598-2

Electronic ISBN: 978-3-030-80599-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-80599-9_16

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