@inproceedings{arora-etal-2022-transfer,
title = "Transfer Learning for Humor Detection by Twin Masked Yellow {M}uppets",
author = {Arora, Aseem and
Dias, Ga{\"e}l and
Jatowt, Adam and
Ekbal, Asif},
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-short.1",
pages = "1--7",
abstract = "Humorous texts can be of different forms such as punchlines, puns, or funny stories. Existing humor classification systems have been dealing with such diverse forms by treating them independently. In this paper, we argue that different forms of humor share a common background either in terms of vocabulary or constructs. As a consequence, it is likely that classification performance can be improved by jointly tackling different humor types. Hence, we design a shared-private multitask architecture following a transfer learning paradigm and perform experiments over four gold standard datasets. Empirical results steadily confirm our hypothesis by demonstrating statistically-significant improvements over baselines and accounting for new state-of-the-art figures for two datasets.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="arora-etal-2022-transfer">
<titleInfo>
<title>Transfer Learning for Humor Detection by Twin Masked Yellow Muppets</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aseem</namePart>
<namePart type="family">Arora</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gaël</namePart>
<namePart type="family">Dias</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Adam</namePart>
<namePart type="family">Jatowt</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Asif</namePart>
<namePart type="family">Ekbal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yulan</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Heng</namePart>
<namePart type="family">Ji</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sujian</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chua-Hui</namePart>
<namePart type="family">Chang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online only</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Humorous texts can be of different forms such as punchlines, puns, or funny stories. Existing humor classification systems have been dealing with such diverse forms by treating them independently. In this paper, we argue that different forms of humor share a common background either in terms of vocabulary or constructs. As a consequence, it is likely that classification performance can be improved by jointly tackling different humor types. Hence, we design a shared-private multitask architecture following a transfer learning paradigm and perform experiments over four gold standard datasets. Empirical results steadily confirm our hypothesis by demonstrating statistically-significant improvements over baselines and accounting for new state-of-the-art figures for two datasets.</abstract>
<identifier type="citekey">arora-etal-2022-transfer</identifier>
<location>
<url>https://aclanthology.org/2022.aacl-short.1</url>
</location>
<part>
<date>2022-11</date>
<extent unit="page">
<start>1</start>
<end>7</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Transfer Learning for Humor Detection by Twin Masked Yellow Muppets
%A Arora, Aseem
%A Dias, Gaël
%A Jatowt, Adam
%A Ekbal, Asif
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F arora-etal-2022-transfer
%X Humorous texts can be of different forms such as punchlines, puns, or funny stories. Existing humor classification systems have been dealing with such diverse forms by treating them independently. In this paper, we argue that different forms of humor share a common background either in terms of vocabulary or constructs. As a consequence, it is likely that classification performance can be improved by jointly tackling different humor types. Hence, we design a shared-private multitask architecture following a transfer learning paradigm and perform experiments over four gold standard datasets. Empirical results steadily confirm our hypothesis by demonstrating statistically-significant improvements over baselines and accounting for new state-of-the-art figures for two datasets.
%U https://aclanthology.org/2022.aacl-short.1
%P 1-7
Markdown (Informal)
[Transfer Learning for Humor Detection by Twin Masked Yellow Muppets](https://aclanthology.org/2022.aacl-short.1) (Arora et al., AACL-IJCNLP 2022)
ACL
- Aseem Arora, Gaël Dias, Adam Jatowt, and Asif Ekbal. 2022. Transfer Learning for Humor Detection by Twin Masked Yellow Muppets. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 1–7, Online only. Association for Computational Linguistics.