| 1. |
- Kniele, Annika, et al.
(author)
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Uppsala University at SemEval-2023 Task12 : Zero-shot Sentiment Classification for Nigerian Pidgin Tweets
- 2023
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In: Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). - : Association for Computational Linguistics. - 9781959429999 ; , s. 1491-1497
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Conference paper (peer-reviewed)abstract
- While sentiment classification has been considered a practically solved task for high-resource languages such as English, the scarcity of data for many languages still makes it a challenging task. The AfriSenti-SemEval shared task aims to classify sentiment on Twitter data for 14 low-resource African languages. In our participation, we focus on Nigerian Pidgin as the target language. We have investigated the effect of English monolingual and multilingual pre-trained models on the sentiment classification task for Nigerian Pidgin. Our setup includes zero-shot models (using English, Igbo and Hausa data) and a Nigerian Pidgin fine-tuned model. Our results show that English fine-tuned models perform slightly better than models fine-tuned on other Nigerian languages, which could be explained by the lexical and structural closeness between Nigerian Pidgin and English. The best results were reported on the monolingual Nigerian Pidgin data. The model pre-trained on English and fine-tuned on Nigerian Pidgin was submitted to Task A Track 4 of the AfriSenti-SemEval Shared Task 12, and scored 25 out of 32 in the ranking.
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| 2. |
- Muhammad, Shamsuddeen Hassan, et al.
(author)
-
SemEval-2023 Task 12 : Sentiment Analysis for African Languages (AfriSenti-SemEval)
- 2023
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In: Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). - : Association for Computational Linguistics. - 9781959429999 ; , s. 2319-2337
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Conference paper (peer-reviewed)abstract
- 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 Yorb (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.
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