@inproceedings{purificato-navigli-2023-apatt,
title = "{AP}att at {S}em{E}val-2023 Task 3: The Sapienza {NLP} System for Ensemble-based Multilingual Propaganda Detection",
author = "Purificato, Antonio and
Navigli, Roberto",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.51",
doi = "10.18653/v1/2023.semeval-1.51",
pages = "382--388",
abstract = "In this paper, we present our approach to the task of identification of persuasion techniques in text, which is a subtask of the SemEval-2023 Task 3 on the multilingual detection of genre, framing, and persuasion techniques in online news. The subtask is multi-label at the paragraph level and the inventory considered by the organizers covers 23 persuasion techniques. Our solution is based on an ensemble of a variety of pre-trained language models (PLMs) fine-tuned on the propaganda dataset. We first describe our system, the different experimental setups we considered, and then provide the results on the dev and test sets released by the organizers. The official evaluation shows our solution ranks 1st in English and attains high scores in all the other languages, i.e. French, German, Italian, Polish, and Russian. We also perform an extensive analysis of the data and the annotations to investigate how they can influence the quality of our systems.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="purificato-navigli-2023-apatt">
<titleInfo>
<title>APatt at SemEval-2023 Task 3: The Sapienza NLP System for Ensemble-based Multilingual Propaganda Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Antonio</namePart>
<namePart type="family">Purificato</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Roberto</namePart>
<namePart type="family">Navigli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Atul</namePart>
<namePart type="given">Kr.</namePart>
<namePart type="family">Ojha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">A</namePart>
<namePart type="given">Seza</namePart>
<namePart type="family">Doğruöz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giovanni</namePart>
<namePart type="family">Da San Martino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Harish</namePart>
<namePart type="family">Tayyar Madabushi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ritesh</namePart>
<namePart type="family">Kumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elisa</namePart>
<namePart type="family">Sartori</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper, we present our approach to the task of identification of persuasion techniques in text, which is a subtask of the SemEval-2023 Task 3 on the multilingual detection of genre, framing, and persuasion techniques in online news. The subtask is multi-label at the paragraph level and the inventory considered by the organizers covers 23 persuasion techniques. Our solution is based on an ensemble of a variety of pre-trained language models (PLMs) fine-tuned on the propaganda dataset. We first describe our system, the different experimental setups we considered, and then provide the results on the dev and test sets released by the organizers. The official evaluation shows our solution ranks 1st in English and attains high scores in all the other languages, i.e. French, German, Italian, Polish, and Russian. We also perform an extensive analysis of the data and the annotations to investigate how they can influence the quality of our systems.</abstract>
<identifier type="citekey">purificato-navigli-2023-apatt</identifier>
<identifier type="doi">10.18653/v1/2023.semeval-1.51</identifier>
<location>
<url>https://aclanthology.org/2023.semeval-1.51</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>382</start>
<end>388</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T APatt at SemEval-2023 Task 3: The Sapienza NLP System for Ensemble-based Multilingual Propaganda Detection
%A Purificato, Antonio
%A Navigli, Roberto
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F purificato-navigli-2023-apatt
%X In this paper, we present our approach to the task of identification of persuasion techniques in text, which is a subtask of the SemEval-2023 Task 3 on the multilingual detection of genre, framing, and persuasion techniques in online news. The subtask is multi-label at the paragraph level and the inventory considered by the organizers covers 23 persuasion techniques. Our solution is based on an ensemble of a variety of pre-trained language models (PLMs) fine-tuned on the propaganda dataset. We first describe our system, the different experimental setups we considered, and then provide the results on the dev and test sets released by the organizers. The official evaluation shows our solution ranks 1st in English and attains high scores in all the other languages, i.e. French, German, Italian, Polish, and Russian. We also perform an extensive analysis of the data and the annotations to investigate how they can influence the quality of our systems.
%R 10.18653/v1/2023.semeval-1.51
%U https://aclanthology.org/2023.semeval-1.51
%U https://doi.org/10.18653/v1/2023.semeval-1.51
%P 382-388
Markdown (Informal)
[APatt at SemEval-2023 Task 3: The Sapienza NLP System for Ensemble-based Multilingual Propaganda Detection](https://aclanthology.org/2023.semeval-1.51) (Purificato & Navigli, SemEval 2023)
ACL