Cross-lingual Open-Retrieval Question Answering for African Languages
Odunayo Ogundepo, Tajuddeen Gwadabe, Clara Rivera, Jonathan Clark, Sebastian Ruder, David Adelani, Bonaventure Dossou, Abdou Diop, Claytone Sikasote, Gilles Hacheme, Happy Buzaaba, Ignatius Ezeani, Rooweither Mabuya, Salomey Osei, Chris Emezue, Albert Kahira, Shamsuddeen Muhammad, Akintunde Oladipo, Abraham Owodunni, Atnafu Tonja, Iyanuoluwa Shode, Akari Asai, Anuoluwapo Aremu, Ayodele Awokoya, Bernard Opoku, Chiamaka Chukwuneke, Christine Mwase, Clemencia Siro, Stephen Arthur, Tunde Ajayi, Verrah Otiende, Andre Rubungo, Boyd Sinkala, Daniel Ajisafe, Emeka Onwuegbuzia, Falalu Lawan, Ibrahim Ahmad, Jesujoba Alabi, Chinedu Mbonu, Mofetoluwa Adeyemi, Mofya Phiri, Orevaoghene Ahia, Ruqayya Iro, Sonia Adhiambo
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 Gwadabe, Clara Rivera, Jonathan Clark, Sebastian Ruder, David Adelani, Bonaventure Dossou, Abdou Diop, Claytone Sikasote, Gilles Hacheme, Happy Buzaaba, Ignatius Ezeani, Rooweither Mabuya, Salomey Osei, Chris Emezue, Albert Kahira, Shamsuddeen Muhammad, Akintunde Oladipo, Abraham Owodunni, et al.. 2023. 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):
- 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 = "Cross-lingual Open-Retrieval Question Answering for {A}frican Languages",
author = "Ogundepo, Odunayo and
Gwadabe, Tajuddeen and
Rivera, Clara and
Clark, Jonathan and
Ruder, Sebastian and
Adelani, David and
Dossou, Bonaventure and
Diop, Abdou and
Sikasote, Claytone and
Hacheme, Gilles and
Buzaaba, Happy and
Ezeani, Ignatius and
Mabuya, Rooweither and
Osei, Salomey and
Emezue, Chris and
Kahira, Albert and
Muhammad, Shamsuddeen and
Oladipo, Akintunde and
Owodunni, Abraham and
Tonja, Atnafu and
Shode, Iyanuoluwa and
Asai, Akari and
Aremu, Anuoluwapo and
Awokoya, Ayodele and
Opoku, Bernard and
Chukwuneke, Chiamaka and
Mwase, Christine and
Siro, Clemencia and
Arthur, Stephen and
Ajayi, Tunde and
Otiende, Verrah and
Rubungo, Andre and
Sinkala, Boyd and
Ajisafe, Daniel and
Onwuegbuzia, Emeka and
Lawan, Falalu and
Ahmad, Ibrahim and
Alabi, Jesujoba and
Mbonu, Chinedu and
Adeyemi, Mofetoluwa and
Phiri, Mofya and
Ahia, Orevaoghene and
Iro, Ruqayya 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 Cross-lingual Open-Retrieval Question Answering for African Languages %A Ogundepo, Odunayo %A Gwadabe, Tajuddeen %A Rivera, Clara %A Clark, Jonathan %A Ruder, Sebastian %A Adelani, David %A Dossou, Bonaventure %A Diop, Abdou %A Sikasote, Claytone %A Hacheme, Gilles %A Buzaaba, Happy %A Ezeani, Ignatius %A Mabuya, Rooweither %A Osei, Salomey %A Emezue, Chris %A Kahira, Albert %A Muhammad, Shamsuddeen %A Oladipo, Akintunde %A Owodunni, Abraham %A Tonja, Atnafu %A Shode, Iyanuoluwa %A Asai, Akari %A Aremu, Anuoluwapo %A Awokoya, Ayodele %A Opoku, Bernard %A Chukwuneke, Chiamaka %A Mwase, Christine %A Siro, Clemencia %A Arthur, Stephen %A Ajayi, Tunde %A Otiende, Verrah %A Rubungo, Andre %A Sinkala, Boyd %A Ajisafe, Daniel %A Onwuegbuzia, Emeka %A Lawan, Falalu %A Ahmad, Ibrahim %A Alabi, Jesujoba %A Mbonu, Chinedu %A Adeyemi, Mofetoluwa %A Phiri, Mofya %A Ahia, Orevaoghene %A Iro, Ruqayya %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)
[Cross-lingual Open-Retrieval Question Answering for African Languages](https://aclanthology.org/2023.findings-emnlp.997) (Ogundepo et al., Findings 2023)
- Cross-lingual Open-Retrieval Question Answering for African Languages (Ogundepo et al., Findings 2023)
ACL
- Odunayo Ogundepo, Tajuddeen Gwadabe, Clara Rivera, Jonathan Clark, Sebastian Ruder, David Adelani, Bonaventure Dossou, Abdou Diop, Claytone Sikasote, Gilles Hacheme, Happy Buzaaba, Ignatius Ezeani, Rooweither Mabuya, Salomey Osei, Chris Emezue, Albert Kahira, Shamsuddeen Muhammad, Akintunde Oladipo, Abraham Owodunni, et al.. 2023. 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.