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Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared. This has lead a struggle to reproduce reported results, and few publicly available benchmarks for African machine translation models exist. To start to address these problems, we trained neural machine translation models for 5 Southern African languages on publicly-available datasets. Code is provided for training the models and evaluate the models on a newly released evaluation set, with the aim of spur future research in the field for Southern African languages.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1906.10511")
get_code_for_paper("1906.10511")
have("1906.10511")
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