Abstract
This paper presents the approaches and systems of the UA-KO team for the Korean portion of SemEval-2022 Task 11 on Multilingual Complex Named Entity Recognition. We fine-tuned Korean and multilingual BERT and RoBERTA models, conducted experiments on data augmentation, ensembles, and task-adaptive pretraining. Our final system ranked 8th out of 17 teams with an F1 score of 0.6749 F1.
Cite
CITATION STYLE
Song, H., & Bethard, S. (2022). UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition. In SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop (pp. 1608–1612). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.semeval-1.222
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
