Abstract
IXA proposes a Sequence labeling fine-tune approach, which consists of a lightweight few-shot baseline (10e), the system takes advantage of transfer learning from pre-trained Named Entity Recognition and cross-lingual knowledge from the LM checkpoint. This technique obtains a drastic reduction in the effective training costs that works as a perfect baseline, future improvements in the baseline approach could fit: 1) Domain adequation, 2) Data augmentation, and 3) Intermediate task learning.- Anthology ID:
- 2023.semeval-1.50
- 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:
- 379–381
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.50
- DOI:
- 10.18653/v1/2023.semeval-1.50
- Cite (ACL):
- Edgar Andres Santamaria. 2023. IXA at SemEval-2023 Task 2: Baseline Xlm-Roberta-base Approach. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 379–381, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- IXA at SemEval-2023 Task 2: Baseline Xlm-Roberta-base Approach (Andres Santamaria, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.semeval-1.50.pdf