@inproceedings{azime-etal-2023-masakhane,
title = "Masakhane-Afrisenti at {S}em{E}val-2023 Task 12: Sentiment Analysis using {A}fro-centric Language Models and Adapters for Low-resource {A}frican Languages",
author = "Azime, Israel Abebe and
Al-Azzawi, Sana Sabah and
Tonja, Atnafu Lambebo and
Shode, Iyanuoluwa and
Alabi, Jesujoba and
Awokoya, Ayodele and
Oduwole, Mardiyyah and
Adewumi, Tosin and
Fanijo, Samuel and
Oyinkansola, Awosan and
Yousuf, Oreen",
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-1.182/",
doi = "10.18653/v1/2023.semeval-1.182",
pages = "1311--1316",
abstract = "Detecting harmful content on social media plat-forms is crucial in preventing the negative ef-fects these posts can have on social media users. This paper presents our methodology for tack-ling task 10 from SemEval23, which focuseson detecting and classifying online sexism insocial media posts. We constructed our solu-tion using an ensemble of transformer-basedmodels (that have been fine-tuned; BERTweet,RoBERTa, and DeBERTa). To alleviate the var-ious issues caused by the class imbalance inthe dataset provided and improve the general-ization of our model, our framework employsdata augmentation and semi-supervised learn-ing. Specifically, we use back-translation fordata augmentation in two scenarios: augment-ing the underrepresented class and augment-ing all classes. In this study, we analyze theimpact of these different strategies on the sys-tem{'}s overall performance and determine whichtechnique is the most effective. Extensive ex-periments demonstrate the efficacy of our ap-proach. For sub-task A, the system achievedan F1-score of 0.8613. The source code to re-produce the proposed solutions is available onGithub"
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="azime-etal-2023-masakhane">
<titleInfo>
<title>Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages</title>
</titleInfo>
<name type="personal">
<namePart type="given">Israel</namePart>
<namePart type="given">Abebe</namePart>
<namePart type="family">Azime</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sana</namePart>
<namePart type="given">Sabah</namePart>
<namePart type="family">Al-Azzawi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Atnafu</namePart>
<namePart type="given">Lambebo</namePart>
<namePart type="family">Tonja</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Iyanuoluwa</namePart>
<namePart type="family">Shode</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jesujoba</namePart>
<namePart type="family">Alabi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ayodele</namePart>
<namePart type="family">Awokoya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mardiyyah</namePart>
<namePart type="family">Oduwole</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tosin</namePart>
<namePart type="family">Adewumi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samuel</namePart>
<namePart type="family">Fanijo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Awosan</namePart>
<namePart type="family">Oyinkansola</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oreen</namePart>
<namePart type="family">Yousuf</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>Detecting harmful content on social media plat-forms is crucial in preventing the negative ef-fects these posts can have on social media users. This paper presents our methodology for tack-ling task 10 from SemEval23, which focuseson detecting and classifying online sexism insocial media posts. We constructed our solu-tion using an ensemble of transformer-basedmodels (that have been fine-tuned; BERTweet,RoBERTa, and DeBERTa). To alleviate the var-ious issues caused by the class imbalance inthe dataset provided and improve the general-ization of our model, our framework employsdata augmentation and semi-supervised learn-ing. Specifically, we use back-translation fordata augmentation in two scenarios: augment-ing the underrepresented class and augment-ing all classes. In this study, we analyze theimpact of these different strategies on the sys-tem’s overall performance and determine whichtechnique is the most effective. Extensive ex-periments demonstrate the efficacy of our ap-proach. For sub-task A, the system achievedan F1-score of 0.8613. The source code to re-produce the proposed solutions is available onGithub</abstract>
<identifier type="citekey">azime-etal-2023-masakhane</identifier>
<identifier type="doi">10.18653/v1/2023.semeval-1.182</identifier>
<location>
<url>https://aclanthology.org/2023.semeval-1.182/</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>1311</start>
<end>1316</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages
%A Azime, Israel Abebe
%A Al-Azzawi, Sana Sabah
%A Tonja, Atnafu Lambebo
%A Shode, Iyanuoluwa
%A Alabi, Jesujoba
%A Awokoya, Ayodele
%A Oduwole, Mardiyyah
%A Adewumi, Tosin
%A Fanijo, Samuel
%A Oyinkansola, Awosan
%A Yousuf, Oreen
%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 azime-etal-2023-masakhane
%X Detecting harmful content on social media plat-forms is crucial in preventing the negative ef-fects these posts can have on social media users. This paper presents our methodology for tack-ling task 10 from SemEval23, which focuseson detecting and classifying online sexism insocial media posts. We constructed our solu-tion using an ensemble of transformer-basedmodels (that have been fine-tuned; BERTweet,RoBERTa, and DeBERTa). To alleviate the var-ious issues caused by the class imbalance inthe dataset provided and improve the general-ization of our model, our framework employsdata augmentation and semi-supervised learn-ing. Specifically, we use back-translation fordata augmentation in two scenarios: augment-ing the underrepresented class and augment-ing all classes. In this study, we analyze theimpact of these different strategies on the sys-tem’s overall performance and determine whichtechnique is the most effective. Extensive ex-periments demonstrate the efficacy of our ap-proach. For sub-task A, the system achievedan F1-score of 0.8613. The source code to re-produce the proposed solutions is available onGithub
%R 10.18653/v1/2023.semeval-1.182
%U https://aclanthology.org/2023.semeval-1.182/
%U https://doi.org/10.18653/v1/2023.semeval-1.182
%P 1311-1316
Markdown (Informal)
[Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages](https://aclanthology.org/2023.semeval-1.182/) (Azime et al., SemEval 2023)
ACL
- Israel Abebe Azime, Sana Sabah Al-Azzawi, Atnafu Lambebo Tonja, Iyanuoluwa Shode, Jesujoba Alabi, Ayodele Awokoya, Mardiyyah Oduwole, Tosin Adewumi, Samuel Fanijo, Awosan Oyinkansola, and Oreen Yousuf. 2023. Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1311–1316, Toronto, Canada. Association for Computational Linguistics.