TY - GEN
T1 - Improving Neural Machine Translation for Low Resource Languages Using Mixed Training
T2 - 21st Mexican International Conference on Artificial Intelligence, MICAI 2022
AU - Tonja, Atnafu Lambebo
AU - Kolesnikova, Olga
AU - Arif, Muhammad
AU - Gelbukh, Alexander
AU - Sidorov, Grigori
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Ethiopian languages
KW - Low-resource machine translation
KW - Machine translation
KW - Mixed training
KW - Neural machine translation
UR - http://www.scopus.com/inward/record.url?scp=85142832025&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-19496-2_3
DO - 10.1007/978-3-031-19496-2_3
M3 - Contribución a la conferencia
AN - SCOPUS:85142832025
SN - 9783031194955
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 30
EP - 40
BT - Advances in Computational Intelligence - 21st Mexican International Conference on Artificial Intelligence, MICAI 2022, Proceedings
A2 - Pichardo Lagunas, Obdulia
A2 - Martínez Seis, Bella
A2 - Martínez-Miranda, Juan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 24 October 2022 through 29 October 2022
ER -