Saheed Abdullahi Salahudeen


2024

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HausaNLP at SemEval-2024 Task 1: Textual Relatedness Analysis for Semantic Representation of Sentences
Saheed Abdullahi Salahudeen | Falalu Ibrahim Lawan | Yusuf Aliyu | Amina Abubakar | Lukman Aliyu | Nur Rabiu | Mahmoud Ahmad | Aliyu Rabiu Shuaibu | Alamin Musa
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

Semantic Text Relatedness (STR), a measure of meaning similarity between text elements, has become a key focus in the field of Natural Language Processing (NLP). We describe SemEval-2024 task 1 on Semantic Textual Relatedness featuring three tracks: supervised learning, unsupervised learning and cross-lingual learning across African and Asian languages including Afrikaans, Algerian Arabic, Amharic, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Punjabi, Spanish, and Telugu. Our goal is to analyse the semantic representation of sentences textual relatedness trained on mBert, all-MiniLM-L6-v2 and Bert-Based-uncased. The effectiveness of these models is evaluated using the Spearman Correlation metric, which assesses the strength of the relationship between paired data. The finding reveals the viability of transformer models in multilingual STR tasks.

2023

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MasakhaNEWS: News Topic Classification for African languages
David Ifeoluwa Adelani | Marek Masiak | Israel Abebe Azime | Jesujoba Alabi | Atnafu Lambebo Tonja | Christine Mwase | Odunayo Ogundepo | Bonaventure F. P. Dossou | Akintunde Oladipo | Doreen Nixdorf | Chris Chinenye Emezue | Sana Al-azzawi | Blessing Sibanda | Davis David | Lolwethu Ndolela | Jonathan Mukiibi | Tunde Ajayi | Tatiana Moteu | Brian Odhiambo | Abraham Owodunni | Nnaemeka Obiefuna | Muhidin Mohamed | Shamsuddeen Hassan Muhammad | Teshome Mulugeta Ababu | Saheed Abdullahi Salahudeen | Mesay Gemeda Yigezu | Tajuddeen Gwadabe | Idris Abdulmumin | Mahlet Taye | Oluwabusayo Awoyomi | Iyanuoluwa Shode | Tolulope Adelani | Habiba Abdulganiyu | Abdul-Hakeem Omotayo | Adetola Adeeko | Abeeb Afolabi | Anuoluwapo Aremu | Olanrewaju Samuel | Clemencia Siro | Wangari Kimotho | Onyekachi Ogbu | Chinedu Mbonu | Chiamaka Chukwuneke | Samuel Fanijo | Jessica Ojo | Oyinkansola Awosan | Tadesse Kebede | Toadoum Sari Sakayo | Pamela Nyatsine | Freedmore Sidume | Oreen Yousuf | Mardiyyah Oduwole | Kanda Tshinu | Ussen Kimanuka | Thina Diko | Siyanda Nxakama | Sinodos Nigusse | Abdulmejid Johar | Shafie Mohamed | Fuad Mire Hassan | Moges Ahmed Mehamed | Evrard Ngabire | Jules Jules | Ivan Ssenkungu | Pontus Stenetorp
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis
Saheed Abdullahi Salahudeen | Falalu Ibrahim Lawan | Ahmad Wali | Amina Abubakar Imam | Aliyu Rabiu Shuaibu | Aliyu Yusuf | Nur Bala Rabiu | Musa Bello | Shamsuddeen Umaru Adamu | Saminu Mohammad Aliyu
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

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.

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HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-information for Multi-level Sexism Classification
Saminu Mohammad Aliyu | Idris Abdulmumin | Shamsuddeen Hassan Muhammad | Ibrahim Said Ahmad | Saheed Abdullahi Salahudeen | Aliyu Yusuf | Falalu Ibrahim Lawan
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain - Reddit) for multilevel classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052%F1-score.