KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition

Caleb Martin, Huichen Yang, William Hsu


Abstract
In this work, we introduce our system to the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) competition. Our team (KDDIE) attempted the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. For this task, we use transfer learning method: fine-tuning the pre-trained language models (PLMs) on the competition dataset. Our two approaches are the BERT-based PLMs and PLMs with additional layer such as Condition Random Field. We report our finding and results in this report.
Anthology ID:
2022.semeval-1.210
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1531–1535
Language:
URL:
https://aclanthology.org/2022.semeval-1.210
DOI:
10.18653/v1/2022.semeval-1.210
Bibkey:
Cite (ACL):
Caleb Martin, Huichen Yang, and William Hsu. 2022. KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1531–1535, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition (Martin et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.210.pdf
Video:
 https://aclanthology.org/2022.semeval-1.210.mp4