Maternal and perinatal mortality figures in 249 South African hospitals 1988 - 1992

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Findings of the WMT'22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages

We present the results of the WMT'22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages. The shared task included both a data and a systems track, along with additional innovations, such as a focus on African languages and extensive human evaluation of submitted systems. We received 14 system submissions from 8 teams, as well as 6 data track contributions. We report a large progress in the quality of translation for African languages since the last iteration of this shared task: there is an increase of about 7.5 BLEU points across 72 language pairs, and the average BLEU scores went from 15.09 to 22.60

Data curation during a pandemic and lessons learned from COVID-19

Detailed, accurate data related to a disease outbreak enable informed public health decision making. Given the variety of data types available across different regions, global data curation and standardization efforts are essential to guarantee rapid data integration and dissemination in times of a pandemic.Data availability The underlying dataset for Fig. 1a is available open access from the supplemental material in ref. 5, and datasets for Fig. 1b,c from the UNESCO World Heritage List 2021 in ref. 32.https://www.nature.com/natcomputscihj2023Computer Scienc

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages

MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages

In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages