Chinedu Emmanuel Mbonu


2024

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The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment
Chris Chinenye Emezue | Ifeoma Okoh | Chinedu Emmanuel Mbonu | Chiamaka Chukwuneke | Daisy Monika Lal | Ignatius Ezeani | Paul Rayson | Ijemma Onwuzulike | Chukwuma Onyebuchi Okeke | Gerald Okey Nweya | Bright Ikechukwu Ogbonna | Chukwuebuka Uchenna Oraegbunam | Esther Chidinma Awo-Ndubuisi | Akudo Amarachukwu Osuagwu
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

The Igbo language is facing a risk of becoming endangered, as indicated by a 2025 UNESCO study. This highlights the need to develop language technologies for Igbo to foster communication, learning and preservation. To create robust, impactful, and widely adopted language technologies for Igbo, it is essential to incorporate the multi-dialectal nature of the language. The primary obstacle in achieving dialectal-aware language technologies is the lack of comprehensive dialectal datasets. In response, we present the IgboAPI dataset, a multi-dialectal Igbo-English dictionary dataset, developed with the aim of enhancing the representation of Igbo dialects. Furthermore, we illustrate the practicality of the IgboAPI dataset through two distinct studies: one focusing on Igbo semantic lexicon and the other on machine translation. In the semantic lexicon project, we successfully establish an initial Igbo semantic lexicon for the Igbo semantic tagger, while in the machine translation study, we demonstrate that by finetuning existing machine translation systems using the IgboAPI dataset, we significantly improve their ability to handle dialectal variations in sentences.

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AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages
Jiayi Wang | David Ifeoluwa Adelani | Sweta Agrawal | Marek Masiak | Ricardo Rei | Eleftheria Briakou | Marine Carpuat | Xuanli He | Sofia Bourhim | Andiswa Bukula | Muhidin Mohamed | Temitayo Olatoye | Tosin Adewumi | Hamam Mokayed | Christine Mwase | Wangui Kimotho | Foutse Yuehgoh | Anuoluwapo Aremu | Jessica Ojo | Shamsuddeen Hassan Muhammad | Salomey Osei | Abdul-Hakeem Omotayo | Chiamaka Chukwuneke | Perez Ogayo | Oumaima Hourrane | Salma El Anigri | Lolwethu Ndolela | Thabiso Mangwana | Shafie Abdi Mohamed | Hassan Ayinde | Oluwabusayo Olufunke Awoyomi | Lama Alkhaled | Sana Al-azzawi | Naome A. Etori | Millicent Ochieng | Clemencia Siro | Njoroge Kiragu | Eric Muchiri | Wangari Kimotho | Lyse Naomi Wamba Momo | Daud Abolade | Simbiat Ajao | Iyanuoluwa Shode | Ricky Macharm | Ruqayya Nasir Iro | Saheed S. Abdullahi | Stephen E. Moore | Bernard Opoku | Zainab Akinjobi | Abeeb Afolabi | Nnaemeka Obiefuna | Onyekachi Raphael Ogbu | Sam Ochieng’ | Verrah Akinyi Otiende | Chinedu Emmanuel Mbonu | Sakayo Toadoum Sari | Yao Lu | Pontus Stenetorp
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments. Learned metrics such as COMET have higher correlation; however, the lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET: COMET evaluation metrics for African languages by leveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-the-art MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441).