  Recent progress in text classification has been focused on high-resource
languages such as English and Chinese. For low-resource languages, amongst them
most African languages, the lack of well-annotated data and effective
preprocessing, is hindering the progress and the transfer of successful
methods. In this paper, we introduce two news datasets (KINNEWS and KIRNEWS)
for multi-class classification of news articles in Kinyarwanda and Kirundi, two
low-resource African languages. The two languages are mutually intelligible,
but while Kinyarwanda has been studied in Natural Language Processing (NLP) to
some extent, this work constitutes the first study on Kirundi. Along with the
datasets, we provide statistics, guidelines for preprocessing, and monolingual
and cross-lingual baseline models. Our experiments show that training
embeddings on the relatively higher-resourced Kinyarwanda yields successful
cross-lingual transfer to Kirundi. In addition, the design of the created
datasets allows for a wider use in NLP beyond text classification in future
studies, such as representation learning, cross-lingual learning with more
distant languages, or as base for new annotations for tasks such as parsing,
POS tagging, and NER. The datasets, stopwords, and pre-trained embeddings are
publicly available at https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus .
