SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
Muhammad, Shamsuddeen Hassan, Abdulmumin, Idris, Yimam, Seid Muhie, Adelani, David Ifeoluwa, Ahmad, Ibrahim Sa'id, Ousidhoum, Nedjma, Ayele, Abinew, Mohammad, Saif M., Beloucif, Meriem, Ruder, Sebastian
–arXiv.org Artificial Intelligence
We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yor\`ub\'a (Muhammad et al., 2023), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches.
arXiv.org Artificial Intelligence
May-1-2023
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