<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="abuzayed-al-khalifa-2021-sarcasm">
    <titleInfo>
        <title>Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Abeer</namePart>
        <namePart type="family">Abuzayed</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hend</namePart>
        <namePart type="family">Al-Khalifa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Sixth Arabic Natural Language Processing Workshop</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Kyiv, Ukraine (Virtual)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we describe our efforts on the shared task of sarcasm and sentiment detection in Arabic (Abu Farha et al., 2021). The shared task consists of two sub-tasks: Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). Our experiments were based on fine-tuning seven BERT-based models with data augmentation to solve the imbalanced data problem. For both tasks, the MARBERT BERT-based model with data augmentation outperformed other models with an increase of the F-score by 15% for both tasks which shows the effectiveness of our approach.</abstract>
    <identifier type="citekey">abuzayed-al-khalifa-2021-sarcasm</identifier>
    <location>
        <url>https://aclanthology.org/2021.wanlp-1.38</url>
    </location>
    <part>
        <date>2021-04</date>
        <extent unit="page">
            <start>312</start>
            <end>317</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation
%A Abuzayed, Abeer
%A Al-Khalifa, Hend
%S Proceedings of the Sixth Arabic Natural Language Processing Workshop
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine (Virtual)
%F abuzayed-al-khalifa-2021-sarcasm
%X In this paper, we describe our efforts on the shared task of sarcasm and sentiment detection in Arabic (Abu Farha et al., 2021). The shared task consists of two sub-tasks: Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). Our experiments were based on fine-tuning seven BERT-based models with data augmentation to solve the imbalanced data problem. For both tasks, the MARBERT BERT-based model with data augmentation outperformed other models with an increase of the F-score by 15% for both tasks which shows the effectiveness of our approach.
%U https://aclanthology.org/2021.wanlp-1.38
%P 312-317

Markdown (Informal)

[Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation](https://aclanthology.org/2021.wanlp-1.38) (Abuzayed & Al-Khalifa, WANLP 2021)

• Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation (Abuzayed & Al-Khalifa, WANLP 2021)

ACL

• Abeer Abuzayed and Hend Al-Khalifa. 2021. Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 312–317, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.