2023
SEMEVAL
SemEval 2023
IXA at SemEval-2023 Task 2: Baseline Xlm-Roberta-base Approach
Abstract
AbstractIXA 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.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Understanding > Named Entity Recognition
Natural Language Processing > Resources & Methods > Multilingual NLP
Machine Learning > Learning Types > Few-Shot Learning
Natural Language Processing > Applications > Named Entity Recognition
Natural Language Processing > Resources & Methods > Transfer Learning
Deep Learning > Learning Types > Transfer Learning