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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.semeval-1.222.pdf
- Data
- KLUE