Natural Language Understanding for African Languages

Abstract

Natural Language Understanding(NLU) is a fundamental building block of goal-oriented conversational AI. In NLU, the two key tasks are predicting the intent of the user’s query and the corresponding slots. Most NLU resources available are for high-resource languages like English. In this paper, we address the limited availability of NLU resources for African languages, most of which are considered Low Resource Languages(LRLs), by presenting the first extension of one the most widely used NLU dataset, the Airline Travel Information Systems (ATIS) dataset to Swahili, Kinyarwanda. We perform baseline experiments using BERT,mBERT, RoBERTa, XLM-RoBERTa under zero-shot settings and achieve promising results. We release the datasets and the annotation tool used for the utterance slot labeling to the community to further NLU research on NLU for African Languages.