Evaluation of Neural Network Transformer Models for Named-Entity Recognition on Low-Resourced Languages
Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 115–119 (2021)
Abstract. In this paper, transformer models are used to evaluate ten low-resourced South African languages for NER. Further, these transformer models are compared to bi-LSTM-aux and CRF models. The transformer models have the highest F-score of 84%. This result is significant within the context of the study, as previous research could not achieve F-scores of 80%. However, the CRF and bi-LSTM-aux models remain top performers in sequence tagging. Transformer models are viable for low-resourced languages. Future research could improve upon these findings by implementing a linear-complexity recurrent transformer variant.
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