AfriQA: Cross-lingual Open-Retrieval Question Answering for African
  Languages
Odunayo Ogundepo Tajuddeen R. Gwadabe Clara E. Rivera Jonathan H. Clark Sebastian Ruder David Ifeoluwa Adelani Bonaventure F. P. Dossou Abdou Aziz DIOP Claytone Sikasote Gilles Hacheme Happy Buzaaba Ignatius Ezeani Rooweither Mabuya Salomey Osei Chris Emezue Albert Njoroge Kahira Shamsuddeen H. Muhammad Akintunde Oladipo Abraham Toluwase Owodunni Atnafu Lambebo Tonja Iyanuoluwa Shode Akari Asai Tunde Oluwaseyi Ajayi Clemencia Siro Steven Arthur Mofetoluwa Adeyemi Orevaoghene Ahia Anuoluwapo Aremu Oyinkansola Awosan Chiamaka Chukwuneke Bernard Opoku Awokoya Ayodele Verrah Otiende Christine Mwase Boyd Sinkala Andre Niyongabo Rubungo Daniel A. Ajisafe Emeka Felix Onwuegbuzia Habib Mbow Emile Niyomutabazi Eunice Mukonde Falalu Ibrahim Lawan Ibrahim Said Ahmad Jesujoba O. Alabi Martin Namukombo Mbonu Chinedu Mofya Phiri Neo Putini Ndumiso Mngoma Priscilla A. Amuok Ruqayya Nasir Iro Sonia Adhiambo
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.