论文标题
Qiaoning在Semeval-2020任务4：基于语言模型集合的常识验证和解释系统
QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model
论文作者
论文摘要
在本文中，我们介绍了提交给Semeval-2020任务4竞赛的语言模型系统：“常识验证和解释”。我们参加了两个子任务，以进行子任务A：验证和子任务B：说明。我们使用预审前的语言模型（BERT，XLNET，ROBERTA和ALBERT）实施了转移学习，并在此任务中对其进行了微调。然后，我们比较了他们在此任务中的特征，以帮助未来的研究人员更正确地理解和使用这些模型。结合模型更好地解决了这个问题，使该模型的准确性达到了子任务A的95.9％，比人类的准确性仅为3％。
In this paper, we present language model system submitted to SemEval-2020 Task 4 competition: "Commonsense Validation and Explanation". We participate in two subtasks for subtask A: validation and subtask B: Explanation. We implemented with transfer learning using pretrained language models (BERT, XLNet, RoBERTa, and ALBERT) and fine-tune them on this task. Then we compared their characteristics in this task to help future researchers understand and use these models more properly. The ensembled model better solves this problem, making the model's accuracy reached 95.9% on subtask A, which just worse than human's by only 3% accuracy.
