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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.00997 |
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| _version_ | 1866910431637929984 |
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| author | Emezue, Chris Chinenye Okoh, Ifeoma Mbonu, Chinedu Chukwuneke, Chiamaka Lal, Daisy Ezeani, Ignatius Rayson, Paul Onwuzulike, Ijemma Okeke, Chukwuma Nweya, Gerald Ogbonna, Bright Oraegbunam, Chukwuebuka Awo-Ndubuisi, Esther Chidinma Osuagwu, Akudo Amarachukwu Nmezi, Obioha |
| author_facet | Emezue, Chris Chinenye Okoh, Ifeoma Mbonu, Chinedu Chukwuneke, Chiamaka Lal, Daisy Ezeani, Ignatius Rayson, Paul Onwuzulike, Ijemma Okeke, Chukwuma Nweya, Gerald Ogbonna, Bright Oraegbunam, Chukwuebuka Awo-Ndubuisi, Esther Chidinma Osuagwu, Akudo Amarachukwu Nmezi, Obioha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00997 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment Emezue, Chris Chinenye Okoh, Ifeoma Mbonu, Chinedu Chukwuneke, Chiamaka Lal, Daisy Ezeani, Ignatius Rayson, Paul Onwuzulike, Ijemma Okeke, Chukwuma Nweya, Gerald Ogbonna, Bright Oraegbunam, Chukwuebuka Awo-Ndubuisi, Esther Chidinma Osuagwu, Akudo Amarachukwu Nmezi, Obioha Computation and Language 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. |
| title | The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.00997 |