MENYO-20k: A Multi-domain English-Yorúbà Corpus for Machine Translation and Domain Adaptation

David I Adelani, Dana Ruiter, Jesujoba Alabi, Damilola Adebonojo, Adesina Ayeni, Mofe Adeyemi, Ayodele Awokoya, Cristina España-Bonet

In: 2nd AfricaNLP Workshop Proceedings, 2021. AfricaNLP Workshop (AfricaNLP-2021) located at EACL 2021 April 19 Virtual ACL 2021.


Massively multilingual machine translation (MT) has shown impressive capabilities, including zero and few-shot translation between low-resource language pairs. However, these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due the lack of standardized evaluation datasets. In this paper, we present MENYO-20k, the first multi-domain parallel corpus for the low-resource Yorùbá-English (yo-en) language pair with standardized train-test splits for benchmarking. We provide several neural MT (NMT) benchmarks on this dataset and compare to the performance of popular pre-trained (massively multilingual) MT models, showing that, in almost all cases, our simple benchmarks outperform the pre-trained MT models. A major gain of BLEU +9.9 and +8.6 (en2yo) is achieved in comparison to Facebook's M2M-100 and Google multilingual NMT respectively when we use MENYO-20k to fine-tune generic models.


menyo.pdf (pdf, 238 KB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz