@inproceedings{kun-etal-2022-logically,
title = "Logically at the Constraint 2022: Multimodal role labelling",
author = "Kun, Ludovic and
Bankoti, Jayesh and
Kiskovski, David",
editor = "Chakraborty, Tanmoy and
Akhtar, Md. Shad and
Shu, Kai and
Bernard, H. Russell and
Liakata, Maria and
Nakov, Preslav and
Srivastava, Aseem",
booktitle = "Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.constraint-1.4",
doi = "10.18653/v1/2022.constraint-1.4",
pages = "24--34",
abstract = "This paper describes our system for the Constraint 2022 challenge at ACL 2022, whose goal is to detect which entities are glorified, vilified or victimised, within a meme . The task should be done considering the perspective of the meme{'}s author. In our work, the challenge is treated as a multi-class classification task. For a given pair of a meme and an entity, we need to classify whether the entity is being referenced as Hero, a Villain, a Victim or Other. Our solution combines (ensembling) different models based on Unimodal (Text only) model and Multimodal model (Text + Images). We conduct several experiments and benchmarks different competitive pre-trained transformers and vision models in this work. Our solution, based on an ensembling method, is ranked first on the leaderboard and obtains a macro F1-score of 0.58 on test set. The code for the experiments and results are available at \url{https://bitbucket.org/logicallydevs/constraint_2022/src/master/}",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kun-etal-2022-logically">
<titleInfo>
<title>Logically at the Constraint 2022: Multimodal role labelling</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ludovic</namePart>
<namePart type="family">Kun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jayesh</namePart>
<namePart type="family">Bankoti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Kiskovski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tanmoy</namePart>
<namePart type="family">Chakraborty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Md.</namePart>
<namePart type="given">Shad</namePart>
<namePart type="family">Akhtar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kai</namePart>
<namePart type="family">Shu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">H</namePart>
<namePart type="given">Russell</namePart>
<namePart type="family">Bernard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Preslav</namePart>
<namePart type="family">Nakov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aseem</namePart>
<namePart type="family">Srivastava</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Dublin, Ireland</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes our system for the Constraint 2022 challenge at ACL 2022, whose goal is to detect which entities are glorified, vilified or victimised, within a meme . The task should be done considering the perspective of the meme’s author. In our work, the challenge is treated as a multi-class classification task. For a given pair of a meme and an entity, we need to classify whether the entity is being referenced as Hero, a Villain, a Victim or Other. Our solution combines (ensembling) different models based on Unimodal (Text only) model and Multimodal model (Text + Images). We conduct several experiments and benchmarks different competitive pre-trained transformers and vision models in this work. Our solution, based on an ensembling method, is ranked first on the leaderboard and obtains a macro F1-score of 0.58 on test set. The code for the experiments and results are available at https://bitbucket.org/logicallydevs/constraint₂022/src/master/</abstract>
<identifier type="citekey">kun-etal-2022-logically</identifier>
<identifier type="doi">10.18653/v1/2022.constraint-1.4</identifier>
<location>
<url>https://aclanthology.org/2022.constraint-1.4</url>
</location>
<part>
<date>2022-05</date>
<extent unit="page">
<start>24</start>
<end>34</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Logically at the Constraint 2022: Multimodal role labelling
%A Kun, Ludovic
%A Bankoti, Jayesh
%A Kiskovski, David
%Y Chakraborty, Tanmoy
%Y Akhtar, Md. Shad
%Y Shu, Kai
%Y Bernard, H. Russell
%Y Liakata, Maria
%Y Nakov, Preslav
%Y Srivastava, Aseem
%S Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kun-etal-2022-logically
%X This paper describes our system for the Constraint 2022 challenge at ACL 2022, whose goal is to detect which entities are glorified, vilified or victimised, within a meme . The task should be done considering the perspective of the meme’s author. In our work, the challenge is treated as a multi-class classification task. For a given pair of a meme and an entity, we need to classify whether the entity is being referenced as Hero, a Villain, a Victim or Other. Our solution combines (ensembling) different models based on Unimodal (Text only) model and Multimodal model (Text + Images). We conduct several experiments and benchmarks different competitive pre-trained transformers and vision models in this work. Our solution, based on an ensembling method, is ranked first on the leaderboard and obtains a macro F1-score of 0.58 on test set. The code for the experiments and results are available at https://bitbucket.org/logicallydevs/constraint₂022/src/master/
%R 10.18653/v1/2022.constraint-1.4
%U https://aclanthology.org/2022.constraint-1.4
%U https://doi.org/10.18653/v1/2022.constraint-1.4
%P 24-34
Markdown (Informal)
[Logically at the Constraint 2022: Multimodal role labelling](https://aclanthology.org/2022.constraint-1.4) (Kun et al., CONSTRAINT 2022)
ACL
- Ludovic Kun, Jayesh Bankoti, and David Kiskovski. 2022. Logically at the Constraint 2022: Multimodal role labelling. In Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations, pages 24–34, Dublin, Ireland. Association for Computational Linguistics.