Abstract
This 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.
Cite
CITATION STYLE
Wu, Y., & Wu, H. (2020). Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation. In 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings (pp. 620–625). International Committee for Computational Linguistics. https://doi.org/10.18653/v1/2020.semeval-1.79
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