What datasets did they use?
English to Afrikaans, isiZulu, N. Sotho,
Setswana, and Xitsonga parallel corpora from the Autshumato project
African languages are numerous, complex and low-resourced. The datasets required for machine translation are difficult to discover, and existing research is hard to reproduce. Minimal attention has been given to machine translation for African languages so there is scant research regarding the problems that arise when using machine translation techniques. To begin addressing these problems, we trained models to translate English to five of the official South African languages (Afrikaans, isiZulu, Northern Sotho, Setswana, Xitsonga), making use of modern neural machine translation techniques. The results obtained show the promise of using neural machine translation techniques for African languages. By providing reproducible publicly-available data, code and results, this research aims to provide a starting point for other researchers in African machine translation to compare to and build upon.
Africa has over 2000 languages across the continent BIBREF0 . South Africa itself has 11 official languages. Unlike many major Western languages, the multitude of African languages are very low-resourced and the few resources that exist are often scattered and difficult to obtain. Machine translation of African languages would not only enable the preservation of such languages, but also empower African citizens to contribute to and learn from global scientific, social and educational conversations, which are currently predominantly English-based BIBREF1 . Tools, such as Google Translate BIBREF2 , support a subset of the official South African languages, namely English, Afrikaans, isiZulu, isiXhosa and Southern Sotho, but do not translate the remaining six official languages. Unfortunately, in addition to being low-resourced, progress in machine translation of African languages has suffered a number of problems. This paper discusses the problems and reviews existing machine translation research for African languages which demonstrate those problems. To try to solve the highlighted problems, we train models to perform machine translation of English to Afrikaans, isiZulu, Northern Sotho (N. Sotho), Setswana and Xitsonga, using state-of-the-art neural machine translation (NMT) architectures, namely, the Convolutional Sequence-to-Sequence (ConvS2S) and Transformer architectures. Section SECREF2 describes the problems facing machine translation for African languages, while the target languages are described in Section SECREF3 . Related work is presented in Section SECREF4 , and the methodology for training machine translation models is discussed in Section SECREF5 . Section SECREF6 presents quantitative and qualitative results.