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Multilingual Language Model Adaptive Fine-Tuning: A Study on African Languages ...
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MasakhaNER: Named entity recognition for African languages
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Adelani, David,; Abbott, Jade; Neubig, Graham; D'Souza, Daniel; Kreutzer, Julia; Lignos, Constantine; Palen-Michel, Chester; Buzaaba, Happy; Rijhwani, Shruti; Ruder, Sebastian; Mayhew, Stephen; Abebe Azime, Israel; Muhammad, Shamsuddeen,; Chinenye Emezue, Chris; Nakatumba-Nabende, Joyce; Ogayo, Perez; Aremu, Anuoluwapo; Gitau, Catherine; Mbaye, Derguene; Alabi, Jesujoba; Yimam, Seid,; Rabiu Gwadabe, Tajuddeen; Ezeani, Ignatius; Niyongabo, Rubungo,; Mukiibi, Jonathan; Otiende, Verrah; Orife, Iroro; David, Davis; Ngom, Samba; Adewumi, Tosin; Rayson, Paul; Adeyemi, Mofetoluwa; Muriuki, Gerald; Anebi, Emmanuel; Chukwuneke, Chiamaka; Odu, Nkiruka; Wairagala, Eric,; Oyerinde, Samuel; Siro, Clemencia; Saul Bateesa, Tobius; Oloyede, Temilola; Wambui, Yvonne; Akinode, Victor; Nabagereka, Deborah; Katusiime, Maurice; Awokoya, Ayodele; Mboup, Mouhamadane; Gebreyohannes, Dibora; Tilaye, Henok; Nwaike, Kelechi; Wolde, Degaga; Faye, Abdoulaye; Sibanda, Blessing; Ahia, Orevaoghene; Dossou, Bonaventure,; Ogueji, Kelechi; Thierno, Ibrahima; DIALLO, Abdoulaye; Akinfaderin, Adewale; Marengereke, Tendai; Osei, Salomey
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021)
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Abstract:
International audience ; We take a step towards addressing the underrepresentation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of stateof-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP. 1
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
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URL: https://hal.inria.fr/hal-03350962
https://doi.org/10.1162/tacl
https://hal.inria.fr/hal-03350962/document
https://hal.inria.fr/hal-03350962/file/adelani_TACL2021.pdf
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The effect of domain and diacritics in Yorùbá-English neural machine translation
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In: 18th Biennial Machine Translation Summit ; https://hal.inria.fr/hal-03350967 ; 18th Biennial Machine Translation Summit, Aug 2021, Orlando, United States (2021)
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The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation ...
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EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT ...
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Transfer learning and distant supervision for multilingual Transformer models: A study on African languages
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In: 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) ; https://hal.inria.fr/hal-03350901 ; 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020, Punta Cana, Dominica (2020)
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Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages ...
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Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi ...
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