Standard
The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment. / Emezue, Chris Chinenye; Okoh, Ifeoma; Mbonu, Chinedu Emmanuel et al.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ed. / Nicoletta Calzolari; Min-Yen Kan; Veronique Hoste; Alessandro Lenci; Sakriani Sakti; Nianwen Xue. Torino, Italia: ELRA and ICCL, 2024. p. 15932-15941.
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Emezue, CC, Okoh, I, Mbonu, CE
, Chukwuneke, C, Lal, DM, Ezeani, I, Rayson, P, Onwuzulike, I, Okeke, CO, Nweya, GO, Ogbonna, BI, Oraegbunam, CU, Awo-Ndubuisi, EC & Osuagwu, AA 2024,
The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment. in N Calzolari, M-Y Kan, V Hoste, A Lenci, S Sakti & N Xue (eds),
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. <
https://aclanthology.org/2024.lrec-main.1384>
APA
Emezue, C. C., Okoh, I., Mbonu, C. E.
, Chukwuneke, C., Lal, D. M., Ezeani, I., Rayson, P., Onwuzulike, I., Okeke, C. O., Nweya, G. O., Ogbonna, B. I., Oraegbunam, C. U., Awo-Ndubuisi, E. C., & Osuagwu, A. A. (2024).
The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.),
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 15932-15941). ELRA and ICCL.
https://aclanthology.org/2024.lrec-main.1384Vancouver
Emezue CC, Okoh I, Mbonu CE
, Chukwuneke C, Lal DM, Ezeani I et al.
The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment. In Calzolari N, Kan MY, Hoste V, Lenci A, Sakti S, Xue N, editors, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). Torino, Italia: ELRA and ICCL. 2024. p. 15932-15941
Author
Emezue, Chris Chinenye ; Okoh, Ifeoma ; Mbonu, Chinedu Emmanuel et al. /
The IgboAPI Dataset : Empowering Igbo Language Technologies through Multi-dialectal Enrichment. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). editor / Nicoletta Calzolari ; Min-Yen Kan ; Veronique Hoste ; Alessandro Lenci ; Sakriani Sakti ; Nianwen Xue. Torino, Italia : ELRA and ICCL, 2024. pp. 15932-15941
Bibtex
@inproceedings{a77e254a45ec4ecca71aaa2ff66935b8,
title = "The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment",
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.",
author = "Emezue, {Chris Chinenye} and Ifeoma Okoh and Mbonu, {Chinedu Emmanuel} and Chiamaka Chukwuneke and Lal, {Daisy Monika} and Ignatius Ezeani and Paul Rayson and Ijemma Onwuzulike 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}",
year = "2024",
month = may,
day = "1",
language = "English",
pages = "15932--15941",
editor = "Nicoletta Calzolari and Min-Yen Kan and Veronique Hoste and Alessandro Lenci and Sakriani Sakti and Nianwen Xue",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
publisher = "ELRA and ICCL",
}
RIS
TY - GEN
T1 - The IgboAPI Dataset
T2 - Empowering Igbo Language Technologies through Multi-dialectal Enrichment
AU - Emezue, Chris Chinenye
AU - Okoh, Ifeoma
AU - Mbonu, Chinedu Emmanuel
AU - Chukwuneke, Chiamaka
AU - Lal, Daisy Monika
AU - Ezeani, Ignatius
AU - Rayson, Paul
AU - Onwuzulike, Ijemma
AU - Okeke, Chukwuma Onyebuchi
AU - Nweya, Gerald Okey
AU - Ogbonna, Bright Ikechukwu
AU - Oraegbunam, Chukwuebuka Uchenna
AU - Awo-Ndubuisi, Esther Chidinma
AU - Osuagwu, Akudo Amarachukwu
PY - 2024/5/1
Y1 - 2024/5/1
N2 - 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.
AB - 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.
M3 - Conference contribution/Paper
SP - 15932
EP - 15941
BT - Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
A2 - Calzolari, Nicoletta
A2 - Kan, Min-Yen
A2 - Hoste, Veronique
A2 - Lenci, Alessandro
A2 - Sakti, Sakriani
A2 - Xue, Nianwen
PB - ELRA and ICCL
CY - Torino, Italia
ER -
