2020
SEMEVAL
SemEval 2020
Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation
Abstract
AbstractThis paper describes our system in subtask A of SemEval 2020 Shared Task 4. We propose a reinforcement learning model based on MTL(Multi-Task Learning) to enhance the prediction ability of commonsense validation. The experimental results demonstrate that our system outperforms the single-task text classification model. We combine MTL and ALBERT pretrain model to achieve an accuracy of 0.904 and our model is ranked 16th on the final leader board of the competition among the 45 teams.
π
Interdisciplinary Bridge
β Artificial Intelligence and Machine Learning
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Cross-Pollinator
β 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