@inproceedings{thin-etal-2023-abcd,
title = "{ABCD} Team at {S}em{E}val-2023 Task 12: An Ensemble Transformer-based System for {A}frican Sentiment Analysis",
author = "Thin, Dang Van and
Nguyen, Dai Ba and
Qui, Dang Ba and
Hao, Duong Ngoc and
Nguyen, Ngan Luu-Thuy",
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-1.44/",
doi = "10.18653/v1/2023.semeval-1.44",
pages = "324--330",
abstract = "This paper describes the system of the ABCD team for three main tasks in the SemEval-2023 Task 12: AfriSenti-SemEval for Low-resource African Languages using Twitter Dataset. We focus on exploring the performance of ensemble architectures based on the soft voting technique and different pre-trained transformer-based language models. The experimental results show that our system has achieved competitive performance in some Tracks in Task A: Monolingual Sentiment Analysis, where we rank the Top 3, Top 2, and Top 4 for the Hause, Igbo and Moroccan languages. Besides, our model achieved competitive results and ranked {\$}14{\textasciicircum}{\{}th{\}}{\$} place in Task B (multilingual) setting and {\$}14{\textasciicircum}{\{}th{\}}{\$} and {\$}8{\textasciicircum}{\{}th{\}}{\$} place in Track 17 and Track 18 of Task C (zero-shot) setting."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="thin-etal-2023-abcd">
<titleInfo>
<title>ABCD Team at SemEval-2023 Task 12: An Ensemble Transformer-based System for African Sentiment Analysis</title>
</titleInfo>
<name type="personal">
<namePart type="given">Dang</namePart>
<namePart type="given">Van</namePart>
<namePart type="family">Thin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dai</namePart>
<namePart type="given">Ba</namePart>
<namePart type="family">Nguyen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dang</namePart>
<namePart type="given">Ba</namePart>
<namePart type="family">Qui</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Duong</namePart>
<namePart type="given">Ngoc</namePart>
<namePart type="family">Hao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ngan</namePart>
<namePart type="given">Luu-Thuy</namePart>
<namePart type="family">Nguyen</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>This paper describes the system of the ABCD team for three main tasks in the SemEval-2023 Task 12: AfriSenti-SemEval for Low-resource African Languages using Twitter Dataset. We focus on exploring the performance of ensemble architectures based on the soft voting technique and different pre-trained transformer-based language models. The experimental results show that our system has achieved competitive performance in some Tracks in Task A: Monolingual Sentiment Analysis, where we rank the Top 3, Top 2, and Top 4 for the Hause, Igbo and Moroccan languages. Besides, our model achieved competitive results and ranked $14⌃{th}$ place in Task B (multilingual) setting and $14⌃{th}$ and $8⌃{th}$ place in Track 17 and Track 18 of Task C (zero-shot) setting.</abstract>
<identifier type="citekey">thin-etal-2023-abcd</identifier>
<identifier type="doi">10.18653/v1/2023.semeval-1.44</identifier>
<location>
<url>https://aclanthology.org/2023.semeval-1.44/</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>324</start>
<end>330</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T ABCD Team at SemEval-2023 Task 12: An Ensemble Transformer-based System for African Sentiment Analysis
%A Thin, Dang Van
%A Nguyen, Dai Ba
%A Qui, Dang Ba
%A Hao, Duong Ngoc
%A Nguyen, Ngan Luu-Thuy
%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 thin-etal-2023-abcd
%X This paper describes the system of the ABCD team for three main tasks in the SemEval-2023 Task 12: AfriSenti-SemEval for Low-resource African Languages using Twitter Dataset. We focus on exploring the performance of ensemble architectures based on the soft voting technique and different pre-trained transformer-based language models. The experimental results show that our system has achieved competitive performance in some Tracks in Task A: Monolingual Sentiment Analysis, where we rank the Top 3, Top 2, and Top 4 for the Hause, Igbo and Moroccan languages. Besides, our model achieved competitive results and ranked $14⌃{th}$ place in Task B (multilingual) setting and $14⌃{th}$ and $8⌃{th}$ place in Track 17 and Track 18 of Task C (zero-shot) setting.
%R 10.18653/v1/2023.semeval-1.44
%U https://aclanthology.org/2023.semeval-1.44/
%U https://doi.org/10.18653/v1/2023.semeval-1.44
%P 324-330Markdown (Informal)
[ABCD Team at SemEval-2023 Task 12: An Ensemble Transformer-based System for African Sentiment Analysis](https://aclanthology.org/2023.semeval-1.44/) (Thin et al., SemEval 2023)
ACL