Papers with African languages
TaTA: A Multilingual Table-to-Text Dataset for African Languages (2023.findings-emnlp)
Copied to clipboard
Sebastian Gehrmann, Sebastian Ruder, Vitaly Nikolaev, Jan Botha, Michael Chavinda, Ankur Parikh, Clara Rivera
| Challenge: | Existing data-to-text generation datasets are limited to English and a small number of other languages. |
| Approach: | They create the first large multilingual table-to-text dataset with a focus on African languages. |
| Outcome: | The proposed dataset includes 8,700 examples in nine languages including four African languages and a zero-shot test language. |
Fumbling in Babel: An Investigation into ChatGPT’s Language Identification Ability (2024.findings-naacl)
Copied to clipboard
| Challenge: | ChatGPT is a powerful NLP tool but its language identification abilities are unclear. |
| Approach: | They compile a benchmark comprising 670 languages representing 23 language families spoken in five continents and compare their language identification abilities to ChatGPT's (both GPT-3.5 and GPT-4) performance. |
| Outcome: | The proposed model performs poorly on African languages, while GPT-3.5 and GPT-4 perform poorly on English, Afrikaans, Arabic, Indonesian, Italian, Mandarin Chinese, and several more. |
Toucan: Many-to-Many Translation for 150 African Language Pairs (2024.findings-acl)
Copied to clipboard
| Challenge: | We introduce two language models with 1.2 billion and 3.7 billion parameters to improve Machine Translation (MT) for low-resource languages. |
| Approach: | They propose a set of tools to improve Machine Translation (MT) for low-resource languages with a focus on African languages. |
| Outcome: | The proposed model outperforms existing models on MT for African languages and improves translation evaluation metrics for 1K languages including African languages. |
Cross-lingual Open-Retrieval Question Answering for African Languages (2023.findings-emnlp)
Copied to clipboard
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
| Challenge: | Our Dataset is the first cross-lingual QA dataset with a focus on African languages. |
| Approach: | They propose to use African languages as the only high-coverage source of answer content for cross-lingual open-retrieval question answering systems. |
| Outcome: | Our Dataset includes 12,000+ XOR QA examples across 10 African languages. |
Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead (2025.emnlp-main)
Copied to clipboard
| Challenge: | African languages are often left behind in state-of-the-art natural language processing systems and large language models. |
| Approach: | They analyze 884 research papers on NLP for African languages published over past five years . they identify key trends shaping the field and outline promising directions . |
| Outcome: | The findings identify key trends shaping the field and outline promising directions . the authors analyze 884 research papers on NLP for African languages published over the past five years . |