2020
EMNLP
EMNLP 2020
SU-NLP at WNUT-2020 Task 2: The Ensemble Models
Abstract
AbstractIn this paper, we address the problem of identifying informative tweets related to COVID-19 in the form of a binary classification task as part of our submission for W-NUT 2020 Task 2. Specifically, we focus on ensembling methods to boost the classification performance of classification models such as BERT and CNN. We show that ensembling can reduce the variance in performance, specifically for BERT base models.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— covid-19 detection
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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
Artificial Intelligence > Core AI > Foundation Models
Machine Learning > Core Methods > Classification
Machine Learning > Application Areas > Model Merging
Natural Language Processing > Applications > Text Classification
Machine Learning > Learning Types > Ensemble Methods
Deep Learning > Models > Transformers
Deep Learning > Learning Types > Deep Learning
Deep Learning > Learning Types > Ensemble Learning