Ahmad Mustapha Wali

Saheed Abdullahi Salahudeen, Falalu Ibrahim Lawan, Ahmad Mustapha Wali et al.

We present the findings of SemEval-2023 Task 12, a shared task on sentiment analysis for low-resource African languages using Twitter dataset. The task featured three subtasks; subtask A is monolingual sentiment classification with 12 tracks which are all monolingual languages, subtask B is multilingual sentiment classification using the tracks in subtask A and subtask C is a zero-shot sentiment classification. We present the results and findings of subtask A, subtask B and subtask C. We also release the code on github. Our goal is to leverage low-resource tweet data using pre-trained Afro-xlmr-large, AfriBERTa-Large, Bert-base-arabic-camelbert-da-sentiment (Arabic-camelbert), Multilingual-BERT (mBERT) and BERT models for sentiment analysis of 14 African languages. The datasets for these subtasks consists of a gold standard multi-class labeled Twitter datasets from these languages. Our results demonstrate that Afro-xlmr-large model performed better compared to the other models in most of the languages datasets. Similarly, Nigerian languages: Hausa, Igbo, and Yoruba achieved better performance compared to other languages and this can be attributed to the higher volume of data present in the languages.

Ahmad Mustapha Wali, Sergiu Nisioi

Hausa texts are often characterized by writing anomalies such as incorrect character substitutions and spacing errors, which sometimes hinder natural language processing (NLP) applications. This paper presents an approach to automatically correct the anomalies by finetuning transformer-based models. Using a corpus gathered from several public sources, we created a large-scale parallel dataset of over 450,000 noisy-clean Hausa sentence pairs by introducing synthetically generated noise, fine-tuned to mimic realistic writing errors. Moreover, we adapted several multilingual and African language-focused models, including M2M100, AfriTEVA, mBART, and Opus-MT variants for this correction task using SentencePiece tokenization. Our experimental results demonstrate significant increases in F1, BLEU and METEOR scores, as well as reductions in Character Error Rate (CER) and Word Error Rate (WER). This research provides a robust methodology, a publicly available dataset, and effective models to improve Hausa text quality, thereby advancing NLP capabilities for the language and offering transferable insights for other low-resource languages.