@inproceedings{dick-etal-2020-humoraac,
title = "{H}umor{AAC} at {S}em{E}val-2020 Task 7: Assessing the Funniness of Edited News Headlines through Regression and Trump Mentions",
author = "Dick, Anna-Katharina and
Weirich, Charlotte and
Kutkina, Alla",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.133/",
doi = "10.18653/v1/2020.semeval-1.133",
pages = "1019--1025",
abstract = "In this paper we describe our contribution to the Semeval-2020 Humor Assessment task. We essentially use three different features that are passed into a ridge regression to determine a funniness score for an edited news headline: statistical, count-based features, semantic features and contextual information. For deciding which one of two given edited headlines is funnier, we additionally use scoring information and logistic regression. Our work was mostly concentrated on investigating features, rather than improving prediction based on pre-trained language models. The resulting system is task-specific, lightweight and performs above the majority baseline. Our experiments indicate that features related to socio-cultural context, in our case mentions of Donald Trump, generally perform better than context-independent features like headline length."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dick-etal-2020-humoraac">
<titleInfo>
<title>HumorAAC at SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines through Regression and Trump Mentions</title>
</titleInfo>
<name type="personal">
<namePart type="given">Anna-Katharina</namePart>
<namePart type="family">Dick</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Charlotte</namePart>
<namePart type="family">Weirich</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alla</namePart>
<namePart type="family">Kutkina</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aurelie</namePart>
<namePart type="family">Herbelot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaodan</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexis</namePart>
<namePart type="family">Palmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathan</namePart>
<namePart type="family">Schneider</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jonathan</namePart>
<namePart type="family">May</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>International Committee for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Barcelona (online)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper we describe our contribution to the Semeval-2020 Humor Assessment task. We essentially use three different features that are passed into a ridge regression to determine a funniness score for an edited news headline: statistical, count-based features, semantic features and contextual information. For deciding which one of two given edited headlines is funnier, we additionally use scoring information and logistic regression. Our work was mostly concentrated on investigating features, rather than improving prediction based on pre-trained language models. The resulting system is task-specific, lightweight and performs above the majority baseline. Our experiments indicate that features related to socio-cultural context, in our case mentions of Donald Trump, generally perform better than context-independent features like headline length.</abstract>
<identifier type="citekey">dick-etal-2020-humoraac</identifier>
<identifier type="doi">10.18653/v1/2020.semeval-1.133</identifier>
<location>
<url>https://aclanthology.org/2020.semeval-1.133/</url>
</location>
<part>
<date>2020-12</date>
<extent unit="page">
<start>1019</start>
<end>1025</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T HumorAAC at SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines through Regression and Trump Mentions
%A Dick, Anna-Katharina
%A Weirich, Charlotte
%A Kutkina, Alla
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F dick-etal-2020-humoraac
%X In this paper we describe our contribution to the Semeval-2020 Humor Assessment task. We essentially use three different features that are passed into a ridge regression to determine a funniness score for an edited news headline: statistical, count-based features, semantic features and contextual information. For deciding which one of two given edited headlines is funnier, we additionally use scoring information and logistic regression. Our work was mostly concentrated on investigating features, rather than improving prediction based on pre-trained language models. The resulting system is task-specific, lightweight and performs above the majority baseline. Our experiments indicate that features related to socio-cultural context, in our case mentions of Donald Trump, generally perform better than context-independent features like headline length.
%R 10.18653/v1/2020.semeval-1.133
%U https://aclanthology.org/2020.semeval-1.133/
%U https://doi.org/10.18653/v1/2020.semeval-1.133
%P 1019-1025
Markdown (Informal)
[HumorAAC at SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines through Regression and Trump Mentions](https://aclanthology.org/2020.semeval-1.133/) (Dick et al., SemEval 2020)
ACL