Linguistically-Motivated Yorùbá-English Machine Translation

Ife Adebara, Muhammad Abdul-Mageed, Miikka Silfverberg


Abstract
Translating between languages where certain features are marked morphologically in one but absent or marked contextually in the other is an important test case for machine translation. When translating into English which marks (in)definiteness morphologically, from Yorùbá which uses bare nouns but marks these features contextually, ambiguities arise. In this work, we perform fine-grained analysis on how an SMT system compares with two NMT systems (BiLSTM and Transformer) when translating bare nouns in Yorùbá into English. We investigate how the systems what extent they identify BNs, correctly translate them, and compare with human translation patterns. We also analyze the type of errors each model makes and provide a linguistic description of these errors. We glean insights for evaluating model performance in low-resource settings. In translating bare nouns, our results show the transformer model outperforms the SMT and BiLSTM models for 4 categories, the BiLSTM outperforms the SMT model for 3 categories while the SMT outperforms the NMT models for 1 category.
Anthology ID:
2022.coling-1.449
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5066–5075
Language:
URL:
https://aclanthology.org/2022.coling-1.449
DOI:
Bibkey:
Cite (ACL):
Ife Adebara, Muhammad Abdul-Mageed, and Miikka Silfverberg. 2022. Linguistically-Motivated Yorùbá-English Machine Translation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5066–5075, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Linguistically-Motivated Yorùbá-English Machine Translation (Adebara et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.449.pdf