[en] This paper presents our submitted system to AfriSenti SemEval-2023 Task 12: Sentiment Analysis for African Languages. The AfriSenti consists of three different tasks, covering monolingual, multilingual, and zero-shot sentiment analysis scenarios for African languages. To improve model generalization, we have explored the following steps: 1) further pre-training of the AfroXLM Pre-trained Language Model (PLM), 2) combining AfroXLM and MARBERT PLMs using a residual layer, and 3) studying the impact of metric learning and two out-of-distribution generalization training objectives. The overall evaluation results show that our system has achieved promising results on several sub-tasks of Task A. For Tasks B and C, our system is ranked among the top six participating systems.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
El Mahdaouy, Abdelkader
Alami, Hamza
LAMSIYAH, Salima ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Berrada, Ismail
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
UM6P at SemEval-2023 Task 12: Out-Of-Distribution Generalization Method for African Languages Sentiment Analysis
Date de publication/diffusion :
2023
Nom de la manifestation :
The 61st Annual Meeting of the Association for Computational Linguistics
Date de la manifestation :
9-14 July 2023
Titre de l'ouvrage principal :
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Maison d'édition :
Association for Computational Linguistics, Toronto, Canada, Inconnu/non spécifié
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