Papers by Hawau Olamide Toyin
Exploring the Limitations of Detecting Machine-Generated Text (2025.coling-main)
Copied to clipboard
| Challenge: | Recent advances in the quality of the generation of text by large language models have spurred research into identifying machine-generated text. |
| Approach: | They audit classification performance for detecting machine-generated text by evaluating on texts with varying writing styles. |
| Outcome: | The proposed methods are highly sensitive to stylistic changes and complexity, and in some cases degrade entirely to random classifiers. |
Voice of a Continent: Mapping Africa’s Speech Technology Frontier (2025.emnlp-main)
Copied to clipboard
AbdelRahim A. Elmadany, Sang Yun Kwon, Hawau Olamide Toyin, Alcides Alcoba Inciarte, Hanan Aldarmaki, Muhammad Abdul-Mageed
| Challenge: | linguistic diversity in Africa is underrepresented in speech technologies, creating barriers to digital inclusion. |
| Approach: | They propose a benchmarking framework to map the continent's linguistic diversity and map its impact on downstream African speech tasks. |
| Outcome: | The proposed model achieves state-of-the-art across multiple African languages and speech tasks. |
Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages (2026.acl-long)
Copied to clipboard
Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida, Irina Nikishina, Ashwath Rao B, Parameswari Krishnamurthy, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi, Nguyen Phan Gia Bao, Amir Hossein Yari, Hawau Olamide Toyin, Nurdaulet Mukhituly, Mena Attia, Besher Hassan, Ahmad Fathan Hidayatullah, Tatsuki Kuribayashi, Haonan Li, Suma Bhat, Fajri Koto
| Challenge: | idioms are a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation. |
| Approach: | They propose a multilingual idiom dataset that provides idiomatic expressions in both sentence-level and conversational contexts. |
| Outcome: | The proposed model performs well with low-resource idioms, but lacks contextual inference. |
Where Are We? Evaluating LLM Performance on African Languages (2025.acl-long)
Copied to clipboard
Ife Adebara, Hawau Olamide Toyin, Nahom Tesfu Ghebremichael, AbdelRahim A. Elmadany, Muhammad Abdul-Mageed
| Challenge: | African languages are underrepresented in NLP due to policies that favor foreign languages and create data inequities. |
| Approach: | They integrate theoretical insights on Africa’s language landscape with an empirical evaluation using Sahara datasets. |
| Outcome: | The proposed model improves on a benchmark curated from large-scale, publicly accessible datasets capturing the continent's linguistic diversity. |
Dialectal Coverage And Generalization in Arabic Speech Recognition (2025.acl-long)
Copied to clipboard
| Challenge: | Existing ASR systems cover the modern standard Arabic variety but fail to cover the multitude of spoken variants. |
| Approach: | They propose a suite of automatic speech recognition models optimized to recognize multiple variants of spoken Arabic. |
| Outcome: | The proposed models show coverage and performance gains compared to prior models. |