MasakhaNER: Named Entity Recognition for African Languages

TL;DR

This paper introduces MasakhaNER, a high-quality NER dataset for ten African languages, addressing under-representation in NLP and enabling future research with comprehensive data, analysis, and evaluation of methods.

Contribution

It provides the first large, publicly available NER dataset for African languages, including analysis and evaluation of state-of-the-art methods in supervised and transfer learning.

Findings

State-of-the-art models perform variably across languages.

Transfer learning improves NER performance in low-resource settings.

The dataset reveals unique linguistic challenges of African languages.

Abstract

We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders. We detail characteristics of the languages to help researchers understand the challenges that these languages pose for NER. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. We release the data, code, and models in order to inspire future research on African NLP.