@inproceedings{bonheme-grzes-2020-sesam,
title = "{SESAM} at {S}em{E}val-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes",
author = "Bonheme, Lisa and
Grzes, Marek",
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-1.102",
doi = "10.18653/v1/2020.semeval-1.102",
pages = "804--816",
abstract = "This paper presents our submission to task 8 (memotion analysis) of the SemEval 2020 competition. We explain the algorithms that were used to learn our models along with the process of tuning the algorithms and selecting the best model. Since meme analysis is a challenging task with two distinct modalities, we studied the impact of different multimodal representation strategies. The results of several approaches to dealing with multimodal data are therefore discussed in the paper. We found that alignment-based strategies did not perform well on memes. Our quantitative results also showed that images and text were uncorrelated. Fusion-based strategies did not show significant improvements and using one modality only (text or image) tends to lead to better results when applied with the predictive models that we used in our research.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bonheme-grzes-2020-sesam">
<titleInfo>
<title>SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lisa</namePart>
<namePart type="family">Bonheme</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marek</namePart>
<namePart type="family">Grzes</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>This paper presents our submission to task 8 (memotion analysis) of the SemEval 2020 competition. We explain the algorithms that were used to learn our models along with the process of tuning the algorithms and selecting the best model. Since meme analysis is a challenging task with two distinct modalities, we studied the impact of different multimodal representation strategies. The results of several approaches to dealing with multimodal data are therefore discussed in the paper. We found that alignment-based strategies did not perform well on memes. Our quantitative results also showed that images and text were uncorrelated. Fusion-based strategies did not show significant improvements and using one modality only (text or image) tends to lead to better results when applied with the predictive models that we used in our research.</abstract>
<identifier type="citekey">bonheme-grzes-2020-sesam</identifier>
<identifier type="doi">10.18653/v1/2020.semeval-1.102</identifier>
<location>
<url>https://aclanthology.org/2020.semeval-1.102</url>
</location>
<part>
<date>2020-12</date>
<extent unit="page">
<start>804</start>
<end>816</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes
%A Bonheme, Lisa
%A Grzes, Marek
%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 bonheme-grzes-2020-sesam
%X This paper presents our submission to task 8 (memotion analysis) of the SemEval 2020 competition. We explain the algorithms that were used to learn our models along with the process of tuning the algorithms and selecting the best model. Since meme analysis is a challenging task with two distinct modalities, we studied the impact of different multimodal representation strategies. The results of several approaches to dealing with multimodal data are therefore discussed in the paper. We found that alignment-based strategies did not perform well on memes. Our quantitative results also showed that images and text were uncorrelated. Fusion-based strategies did not show significant improvements and using one modality only (text or image) tends to lead to better results when applied with the predictive models that we used in our research.
%R 10.18653/v1/2020.semeval-1.102
%U https://aclanthology.org/2020.semeval-1.102
%U https://doi.org/10.18653/v1/2020.semeval-1.102
%P 804-816
Markdown (Informal)
[SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes](https://aclanthology.org/2020.semeval-1.102) (Bonheme & Grzes, SemEval 2020)
ACL