Papers by Claytone Sikasote
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets (2022.tacl-1)
Copied to clipboard
Julia Kreutzer, Isaac Caswell, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Ortiz Suarez, Iroro Orife, Kelechi Ogueji, Andre Niyongabo Rubungo, Toan Q. Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, Mofetoluwa Adeyemi
| Challenge: | Lower-resource corpora have systematic issues, including mislabeled or nonstandard/ambiguous language codes. |
| Approach: | They manually audit the quality of 205 language-specific corpora released with five major public datasets. |
| Outcome: | The results show that lower-resource corpora have systematic issues even for non-proficient speakers. |
BIG-C: a Multimodal Multi-Purpose Dataset for Bemba (2023.acl-long)
Copied to clipboard
| Challenge: | Bemba is the most populous language of Zambia but lacks resources for research . despite its significance, Bemba remains under-resourced and lacking in high-quality data and resources for NLP experiments and language technologies. |
| Approach: | They propose a large multimodal dataset for Bemba that includes images, transcriptions and translations. |
| Outcome: | The proposed dataset is based on images, transcriptions and translations of Bemba speakers . it provides baselines on speech recognition, machine translation and speech translation tasks . |
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. |
BembaSpeech: A Speech Recognition Corpus for the Bemba Language (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing speech recognition systems for African languages are very low . lack of resources (speech and text) can be attributed to poor quality of speech. |
| Approach: | They present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting of 24 hours of read speech in the Bemba language. |
| Outcome: | The proposed model achieves a word error rate (WER) of 32.91% on the Bemba language . the 1 billion XLS-R parameter model achieve better performance than the monolingual pre-trained English model on the corpus. |