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

A Study of the Relationship Between Performance and Motivation in Informal Trade (A Case of Grocery Shops in Selected Markets in Lusaka District of Zambia)

Introduction: Some traders do succeed while others fail and others remain on the same level, or even fail, given that all players operate in the same business environment. Initiatives to reduce on informal trading and promote formalization of businesses are top on national agendas. Aim of the study: The study aims to identify those values that are being held by successful entrepreneurs in informal trade. Methodology: Sample size of 297 respondents were chosen from the centrally placed six markets in Lusaka district. The study employs a mixed method design which hinges on correlation of motivation with performance. Annual sales, rather than monthly are chosen, for annual figures reflect a period long enough to gather reliable information for purposes of analysis. Increase in sales has been used as a measure of growth. A questionnaire is the main instrument of research. Findings: There is a strong positive correlation (r = 0.82) between Annual sales and ‘other’ motivation among informal traders. Discussion: A blend of motivational values enhances performance among informal trader. Conclusion: Government should package a blend of motivational influences and replicate such to make other traders grow and transition into formal businesses

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