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"Penelitian ini mengusulkan pengembangan model monolingual untuk Natural Language Inference (NLI) dalam bahasa Swahili untuk mengatasi keterbatasan model multibahasa saat ini. Studi ini melakukan fine-tuning pada model SwahBERT yang sudah dilatih sebelumnya untuk menangkap hubungan semantik dan nuansa kontekstual unik dalam bahasa Swahili. Komponen penting dari penelitian ini adalah pembuatan dataset SwahiliNLI, yang dirancang untuk mencerminkan kompleksitas bahasa Swahili, sehingga menghindari ketergantungan pada teks bahasa Inggris yang diterjemahkan. Selain itu, kinerja model SwahBERT yang telah di-fine-tune dievaluasi menggunakan dataset SwahiliNLI dan XNLI, dan dibandingkan dengan model multibahasa mBERT. Hasilnya menunjukkan bahwa model SwahBERT mengungguli model multibahasa, mencapai tingkat akurasi sebesar 78,78% pada dataset SwahiliNLI dan 73,51% pada dataset XNLI. Model monolingual juga menunjukkan presisi, recall, dan skor F1 yang lebih baik, terutama dalam mengenali pola linguistik dan memprediksi pasangan kalimat. Penelitian ini menekankan pentingnya menggunakan dataset yang dihasilkan secara manual dan model monolingual dalam bahasa dengan sumber daya rendah, memberikan wawasan berharga untuk pengembangan sistem NLI yang lebih efisien dan relevan secara kontekstual, sehingga memajukan pemrosesan bahasa alami untuk bahasa Swahili dan berpotensi menguntungkan bahasa lain yang menghadapi keterbatasan sumber daya serupa.

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This research proposes the development of a monolingual model for Natural Language Inference (NLI) in Swahili to overcome the limitations of current multilingual models. The study fine-tunes the pre-trained SwahBERT model to capture Swahili's unique semantic relationships and contextual nuances. A critical component of this research is the creation of a SwahiliNLI dataset, crafted to reflect the intricacies of the language, thereby avoiding reliance on translated English text. Furthermore, the performance of the fine-tuned SwahBERT model is evaluated using both SwahiliNLI and the XNLI dataset, and compared with the multilingual mBERT model. The results reveal that the SwahBERT model outperforms the multilingual model, achieving an accuracy rate of 78.78% on the SwahiliNLI dataset and 73.51% on the XNLI dataset. The monolingual model also exhibits superior precision, recall, and F1 scores, particularly in recognizing linguistic patterns and predicting sentence pairings. This research underscores the importance of using manually generated datasets and monolingual models in low-resource languages, providing valuable insights for the development of more efficient and contextually relevant NLI systems, thereby advancing natural language processing for Swahili and potentially benefiting other languages facing similar resource constraints."

"ABSTRACTThe aim of this brief article is to highlight one aspect of Swahili Culture, that is the aspect of Family and Kinship relations under the title: Dhana na Dhima ya Familia na Unasaba katika Utamaduni wa Kiswahili (The Concept and Role of Family and Kinship in Swahili Culture). We define Culture as the sum total of social behaviours, traditions, customs and a total way of life of a particular society. Culture looks at the regular practices of the given society such as; when or what they eat, their dress codes, greetings, their etiquettes, taboos, their beliefs and so on (Isack 2018:105). This article is the continued efforts of the writer in enriching the knowledge on Swahili Culture and African Culture at large. As we mentioned in the previous article: Makuzi na Hatua za Maisha katika Utamaduni wa Kiswahili (The Upbringing and Life Stages in Swahili Culture) (Isack 2018:105), there are challenges in the instruction of Swahili Language and its Culture to foreign learners due to the lack of enough reference materials designed for them. Thus, both instructors and learners of Swahili as a foreign language can utilize this work to narrow the current knowledge gap."

"The early history of nautical technology in the western Indian Ocean and adjoining parts of the eastern Africa coast is poorly understood. In the absence of evidence from shipwrecks, it has hitherto been based largely on the uncertain interpretation of a few documentary references and speculation surrounding technological parallels and assumed lexical resemblances. This paper examines some of the linguistic evidence in a more rigorous way, by undertaking a cross-dialectal comparison of names for watercraft and terms for outriggers in Swahili (Kiswahili), a Bantu language spoken on the islands and in scattered communities along the western seaboard of the Indian Ocean. The resulting analysis provides a new outline of the development of Swahili nautical technology and maritime culture, highlighting the key role played by particular boat forms, and the relative importance of indigenous innovation and different external influences, including the elusive impacts of Austronesian seafaring."