@inproceedings{inacio-etal-2023-humor,
title = "What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models",
author = "In{\'a}cio, Marcio and
Wick-pedro, Gabriela and
Goncalo Oliveira, Hugo",
editor = "Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.latechclfl-1.10",
doi = "10.18653/v1/2023.latechclfl-1.10",
pages = "88--98",
abstract = "Towards computational systems capable of dealing with complex and general linguistic phenomena, it is essential to understand figurative language, which verbal humor is an instance of. This paper reports state-of-the-art results for Humor Recognition in Portuguese, specifically, an F1-score of 99.64{\%} with a BERT-based classifier. However, following the surprising high performance in such a challenging task, we further analyzed what was actually learned by the classifiers. Our main conclusions were that classifiers based on content-features achieve the best performance, but rely mostly on stylistic aspects of the text, not necessarily related to humor, such as punctuation and question words. On the other hand, for humor-related features, we identified some important aspects, such as the presence of named entities, ambiguity and incongruity.",
}
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<abstract>Towards computational systems capable of dealing with complex and general linguistic phenomena, it is essential to understand figurative language, which verbal humor is an instance of. This paper reports state-of-the-art results for Humor Recognition in Portuguese, specifically, an F1-score of 99.64% with a BERT-based classifier. However, following the surprising high performance in such a challenging task, we further analyzed what was actually learned by the classifiers. Our main conclusions were that classifiers based on content-features achieve the best performance, but rely mostly on stylistic aspects of the text, not necessarily related to humor, such as punctuation and question words. On the other hand, for humor-related features, we identified some important aspects, such as the presence of named entities, ambiguity and incongruity.</abstract>
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%0 Conference Proceedings
%T What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models
%A Inácio, Marcio
%A Wick-pedro, Gabriela
%A Goncalo Oliveira, Hugo
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F inacio-etal-2023-humor
%X Towards computational systems capable of dealing with complex and general linguistic phenomena, it is essential to understand figurative language, which verbal humor is an instance of. This paper reports state-of-the-art results for Humor Recognition in Portuguese, specifically, an F1-score of 99.64% with a BERT-based classifier. However, following the surprising high performance in such a challenging task, we further analyzed what was actually learned by the classifiers. Our main conclusions were that classifiers based on content-features achieve the best performance, but rely mostly on stylistic aspects of the text, not necessarily related to humor, such as punctuation and question words. On the other hand, for humor-related features, we identified some important aspects, such as the presence of named entities, ambiguity and incongruity.
%R 10.18653/v1/2023.latechclfl-1.10
%U https://aclanthology.org/2023.latechclfl-1.10
%U https://doi.org/10.18653/v1/2023.latechclfl-1.10
%P 88-98
Markdown (Informal)
[What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models](https://aclanthology.org/2023.latechclfl-1.10) (Inácio et al., LaTeCHCLfL 2023)
ACL