Abstract
In the article, we present the CodeNLP submission to the SemEval-2023 Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition. Our approach is based on data augmentation by combining various strategies of sequence generation for training. We show that the extended procedure of fine-tuning a pre-trained language model can bring improvements compared to any single strategy. On the development subsets, the improvements were 1.7 pp and 3.1 pp of F-measure, for English and multilingual datasets, respectively. On the test subsets our models achieved 63.51% and 73.22% of Macro F1, respectively.- Anthology ID:
- 2023.semeval-1.249
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1798–1804
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.249
- DOI:
- 10.18653/v1/2023.semeval-1.249
- Cite (ACL):
- Micha Marcińczuk and Wiktor Walentynowicz. 2023. CodeNLP at SemEval-2023 Task 2: Data Augmentation for Named Entity Recognition by Combination of Sequence Generation Strategies. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1798–1804, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- CodeNLP at SemEval-2023 Task 2: Data Augmentation for Named Entity Recognition by Combination of Sequence Generation Strategies (Marcińczuk & Walentynowicz, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.semeval-1.249.pdf