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

AfriQA:Cross-lingual Open-Retrieval Question Answering for African Languages

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while serving people in their native language -- offer a means of filling this gap. To this end, we create AfriQA, the first cross-lingual QA dataset with a focus on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, AfriQA focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology

Intestinal schistosomiasis and contributory risk factors in Nsu, Ehime Mbano LGA Imo State, Nigeria

The prevalence of intestinal schistosomiasis was determined in three communities in Nsu, Ehime Mbano Local Government Area, Imo State using structured questionnaires for demographic data collection and faecal analysis for detecting the presence of Schistosoma mansoni ova. Examination for the presence of S. mansoni was done using formol-ether concentration technique and microscopy. Out of the 476 persons examined comprising 268 males and 208 females, 24 (5.04%) had their faecal samples positive for S. mansoni infection. More males (5.97%) than females (3.85%) were infected. The highest prevalence occurred in persons aged 15-29 years (5.82%) followed by persons aged 0-14 years (5.76%). Infected persons were mostly students (5.67%) and farmers (5.55%). There was a significant association between occupation and infection(x2 51.84, p>0.05). Source of water used seemed to play a contributory role to infection. Infection occurred most among those that used the stream as their source of water (6.73%). Regarding toilet facilities, those who defaecated in the bush had the highest rate of infection (5.37%). The result of this study has revealed the presence of intestinal schistosomiasis in Nsu. It would be aptly appropriate for the infected persons to seek treatment at the local health centre and the government possibly consider including Nsu in the schistosomiasis control programme, though prevalence was below 50%. This is to help prevent further spread of the infection and control possible morbidity and resultant socio-economic effects that may arise there from.Keywords: intestinal schistosomiasis, Nsu, prevalence, risk factors

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

AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while serving people in their native language -- offer a means of filling this gap. To this end, we create AfriQA, the first cross-lingual QA dataset with a focus on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, AfriQA focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology