Adelani, DI;
Neubig, G;
Ruder, S;
Rijhwani, S;
Beukman, M;
Palen-Michel, C;
Lignos, C;
... Klakow, D; + view all
(2022)
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition.
In:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022.
(pp. pp. 4488-4508).
Association for Computational Linguistics: Abu Dhabi, United Arab Emirates.
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Abstract
African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages.
| Type: | Proceedings paper |
|---|---|
| Title: | MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition |
| Event: | 2022 Conference on Empirical Methods in Natural Language Processing |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://aclanthology.org/2022.emnlp-main.298/ |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10167286 |
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