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 Hassan 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. Amouk, Ruqayya Nasir Iro, Sonia Adhiambo
Correct Metadata for
Abstract
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 Our Dataset, the first cross-lingual QA dataset with a focus on African languages. Our Dataset 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, Our Dataset 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, Our Dataset proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology.- Anthology ID:
- 2023.findings-emnlp.997
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 14957–14972
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.997/
- DOI:
- 10.18653/v1/2023.findings-emnlp.997
- Bibkey:
- Cite (ACL):
- 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 Hassan 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. Amouk, Ruqayya Nasir Iro, and Sonia Adhiambo. 2023. AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14957–14972, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages (Ogundepo et al., Findings 2023)
- Copy Citation:
- PDF:
- https://aclanthology.org/2023.findings-emnlp.997.pdf
Export citation
@inproceedings{ogundepo-etal-2023-cross,
title = "{AfriQA}: Cross-lingual Open-Retrieval Question Answering for {A}frican Languages",
author = "Ogundepo, Odunayo and
Gwadabe, Tajuddeen R. and
Rivera, Clara E. and
Clark, Jonathan H. and
Ruder, Sebastian and
Adelani, David Ifeoluwa and
Dossou, Bonaventure F. P. and
Diop, Abdou Aziz and
Sikasote, Claytone and
Hacheme, Gilles and
Buzaaba, Happy and
Ezeani, Ignatius and
Mabuya, Rooweither and
Osei, Salomey and
Emezue, Chris and
Kahira, Albert Njoroge and
Muhammad, Shamsuddeen Hassan and
Oladipo, Akintunde and
Owodunni, Abraham Toluwase and
Tonja, Atnafu Lambebo and
Shode, Iyanuoluwa and
Asai, Akari and
Ajayi, Tunde Oluwaseyi and
Siro, Clemencia and
Arthur, Steven and
Adeyemi, Mofetoluwa and
Ahia, Orevaoghene and
Aremu, Anuoluwapo and
Awosan, Oyinkansola and
Chukwuneke, Chiamaka and
Opoku, Bernard and
Ayodele, Awokoya and
Otiende, Verrah and
Mwase, Christine and
Sinkala, Boyd and
Rubungo, Andre Niyongabo and
Ajisafe, Daniel A. and
Onwuegbuzia, Emeka Felix and
Mbow, Habib and
Niyomutabazi, Emile and
Mukonde, Eunice and
Lawan, Falalu Ibrahim and
Ahmad, Ibrahim Said and
Alabi, Jesujoba O. and
Namukombo, Martin and
Chinedu, Mbonu and
Phiri, Mofya and
Putini, Neo and
Mngoma, Ndumiso and
Amouk, Priscilla A. and
Iro, Ruqayya Nasir and
Adhiambo, Sonia",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.997/",
doi = "10.18653/v1/2023.findings-emnlp.997",
pages = "14957--14972",
abstract = "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 Our Dataset, the first cross-lingual QA dataset with a focus on African languages. Our Dataset 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, Our Dataset 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, Our Dataset proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology."
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<abstract>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 Our Dataset, the first cross-lingual QA dataset with a focus on African languages. Our Dataset 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, Our Dataset 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, Our Dataset proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology.</abstract>
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%0 Conference Proceedings %T AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages %A Ogundepo, Odunayo %A Gwadabe, Tajuddeen R. %A Rivera, Clara E. %A Clark, Jonathan H. %A Ruder, Sebastian %A Adelani, David Ifeoluwa %A Dossou, Bonaventure F. P. %A Diop, Abdou Aziz %A Sikasote, Claytone %A Hacheme, Gilles %A Buzaaba, Happy %A Ezeani, Ignatius %A Mabuya, Rooweither %A Osei, Salomey %A Emezue, Chris %A Kahira, Albert Njoroge %A Muhammad, Shamsuddeen Hassan %A Oladipo, Akintunde %A Owodunni, Abraham Toluwase %A Tonja, Atnafu Lambebo %A Shode, Iyanuoluwa %A Asai, Akari %A Ajayi, Tunde Oluwaseyi %A Siro, Clemencia %A Arthur, Steven %A Adeyemi, Mofetoluwa %A Ahia, Orevaoghene %A Aremu, Anuoluwapo %A Awosan, Oyinkansola %A Chukwuneke, Chiamaka %A Opoku, Bernard %A Ayodele, Awokoya %A Otiende, Verrah %A Mwase, Christine %A Sinkala, Boyd %A Rubungo, Andre Niyongabo %A Ajisafe, Daniel A. %A Onwuegbuzia, Emeka Felix %A Mbow, Habib %A Niyomutabazi, Emile %A Mukonde, Eunice %A Lawan, Falalu Ibrahim %A Ahmad, Ibrahim Said %A Alabi, Jesujoba O. %A Namukombo, Martin %A Chinedu, Mbonu %A Phiri, Mofya %A Putini, Neo %A Mngoma, Ndumiso %A Amouk, Priscilla A. %A Iro, Ruqayya Nasir %A Adhiambo, Sonia %Y Bouamor, Houda %Y Pino, Juan %Y Bali, Kalika %S Findings of the Association for Computational Linguistics: EMNLP 2023 %D 2023 %8 December %I Association for Computational Linguistics %C Singapore %F ogundepo-etal-2023-cross %X 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 Our Dataset, the first cross-lingual QA dataset with a focus on African languages. Our Dataset 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, Our Dataset 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, Our Dataset proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology. %R 10.18653/v1/2023.findings-emnlp.997 %U https://aclanthology.org/2023.findings-emnlp.997/ %U https://doi.org/10.18653/v1/2023.findings-emnlp.997 %P 14957-14972
Markdown (Informal)
[AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages](https://aclanthology.org/2023.findings-emnlp.997/) (Ogundepo et al., Findings 2023)
- AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages (Ogundepo et al., Findings 2023)
ACL
- 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 Hassan 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. Amouk, Ruqayya Nasir Iro, and Sonia Adhiambo. 2023. AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14957–14972, Singapore. Association for Computational Linguistics.