KINNEWS and KIRNEWS : Benchmarking Cross-Lingual Text Classification for K inyarwanda and K irundi (2020)

Abstract: 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 IRNEWS) 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 .

## introduction the availability of large monolingual and labeled corpora, paired with innovations in neural text processing have led to a rapid improvement of the quality of text classification over the last years. 1 however, the effectiveness of deep-learning-based text classification models depends on the amount of monolingual and labeled data. low-resource languages are traditionally left behind because of the few available prepared resources for these languages to extract the data from @xcite . however, nowadays, the increase in internet use in many african developing countries has made access to information easier. this in turn has strengthened the news agencies of those countries to cover many stories in their native languages. for example, bbc news now provides online news in arabic, amharic, hausa, kiswahili, somali, oromo, igbo, nigerian pidgin, tigrigna, kinyarwanda and kirundi. 2 this development makes news the most reliable source of data for low-resource languages. we explore this opportunity for the example of kinyarwanda and kirundi, two african low-resource bantu languages, and build news classification benchmarks from online news articles. this has the goal to enable nlp researchers to include kinyarwanda and kirundi in the evaluation of novel text classification approaches, and diversify the current nlp landscape. kinyarwanda is one of the official languages of rwanda 3 and belongs to the niger-congo language family. according to the new times, 4 it is spoken by approximately 30 million people from four dif-ferent african countries: rwanda, uganda, dr congo, and tanzania. kirundi is an official language of burundi, a neighboring country of rwanda, and it is spoken by at least 9 million people. 5 kinyarwanda and kirundi are mutually intelligible. ## dataset creation in this section, we first describe the process for data collection, then the annotation, and finally our data cleaning pipeline for kinnews and kirnews. in general, the copyright for the published news articles still remains with the original authors or publishers. our work can be seen as an additional pre-processing and modeling pipeline, and annotation layer on top. ## conclusion and future work in this paper, we built the first news text classification benchmark for kinyarwanda and kirundi, two low-resource bantu languages. we described the data collection process, provided guidelines for data cleaning, and evaluated classic text classification models as initial baselines. we found fairly strong cross-lingual generation of embedding models trained on the resource-richer kinyarwanda to the lowerresource kirundi due to their mutual intelligibility. this gives hope for future studies of languages from the rwanda-rundi language family, which have not been studied in nlp at all, and would otherwise classify as "left-behind" according to @xcite , or analogously of other extremely low-resource languages with a slightly higher-resource "sibling". future studies on the new dataset will investigate (1) contextualized embeddings, e.g. bert, (2) subword modeling. furthermore, the dataset may get enriched with other linguistic annotations, such as named entities, and serve as resource for other nlp tasks than text classification.