@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 and
Nguyen, Dai and
Qui, Dang and
Hao, Duong and
Nguyen, Ngan",
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{\^{}}{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.",
}
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<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>
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%0 Conference Proceedings
%T ABCD Team at SemEval-2023 Task 12: An Ensemble Transformer-based System for African Sentiment Analysis
%A Thin, Dang
%A Nguyen, Dai
%A Qui, Dang
%A Hao, Duong
%A Nguyen, Ngan
%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 324-330
Markdown (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