The IgboAPI Dataset : Empowering Igbo Language Technologies through Multi-dialectal Enrichment

Emezue, Chris Chinenye and Okoh, Ifeoma and Mbonu, Chinedu Emmanuel and Chukwuneke, Chiamaka and Lal, Daisy Monika and Ezeani, Ignatius and Rayson, Paul and Onwuzulike, Ijemma and Okeke, Chukwuma Onyebuchi and Nweya, Gerald Okey and Ogbonna, Bright Ikechukwu and Oraegbunam, Chukwuebuka Uchenna and Awo-Ndubuisi, Esther Chidinma and Osuagwu, Akudo Amarachukwu (2024) The IgboAPI Dataset : Empowering Igbo Language Technologies through Multi-dialectal Enrichment. In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) :. ELRA and ICCL, Torino, Italia, pp. 15932-15941.

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Abstract

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.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
Research Output Funding/no_not_funded
Subjects:
?? no - not funded ??
ID Code:
220732
Deposited By:
Deposited On:
07 Jun 2024 15:50
Refereed?:
Yes
Published?:
Published
Last Modified:
17 Jun 2024 23:49