Abstract
Neural Machine Translation (NMT) has shown improvement for high-resource languages, but there is still a problem with low-resource languages as NMT performs well on huge parallel data available for high-resource languages. In spite of many proposals to solve the problem of low-resource languages, it continues to be a difficult challenge. The issue becomes even more complicated when few resources cover only one domain. In our attempt to combat this issue, we propose a new approach to improve NMT for low-resource languages. The proposed approach using the transformer model shows 5.3, 5.0, and 3.7 BLEU score improvement for Gamo-English, Gofa-English, and Dawuro-English language pairs, respectively, where Gamo, Gofa, and Dawuro are related low-resource Ethiopian languages. We discuss our contributions and envisage future steps in this challenging research area.
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Acknowledgments
The work was done with partial support from the Mexican Government through the grant A1S-47854 of CONACYT, Mexico, grants 20220852, 20220859, and 20221627 of the Secretaría de Investigación y Posgrado of the Instituto Politécnico Nacional, Mexico. The authors thank the CONACYT for the computing resources brought to them through the Plataforma de Aprendizaje Profundo para Tecnologías del Lenguaje of the Laboratorio de Supercómputo of the INAOE, Mexico and acknowledge the support of Microsoft through the Microsoft Latin America PhD Award.
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Tonja, A.L., Kolesnikova, O., Arif, M., Gelbukh, A., Sidorov, G. (2022). Improving Neural Machine Translation for Low Resource Languages Using Mixed Training: The Case of Ethiopian Languages. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13613. Springer, Cham. https://doi.org/10.1007/978-3-031-19496-2_3
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