UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition

Hyunju Song, Steven Bethard


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.
Anthology ID:
2022.semeval-1.222
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1608–1612
Language:
URL:
https://aclanthology.org/2022.semeval-1.222
DOI:
10.18653/v1/2022.semeval-1.222
Bibkey:
Cite (ACL):
Hyunju Song and Steven Bethard. 2022. UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1608–1612, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition (Song & Bethard, SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.222.pdf
Video:
 https://aclanthology.org/2022.semeval-1.222.mp4
Data
KLUE