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

KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi