论文标题
CUHK在Semeval-2020任务4：通过多任务学习的共识解释，推理和预测
CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Prediction with Multi-task Learning
论文作者
论文摘要
本文介绍了我们提交给Semeval 2020任务4的系统：常识验证和解释（comve），该（Comve）由三个子任务组成。任务是直接验证给定的句子是否有意义并要求模型来解释它。基于具有多任务设置的Bertharchittuct，我们提出了一个有效且可解释的“解释，理由和预测”（ERP）系统，以求解有关常识的三个子任务：（a）验证，（b）推理和（c）解释。受常识的认知研究的启发，我们的系统首先产生了对句子的原因或理解，然后选择了哪种陈述有意义，这是通过多任务学习实现的。在评估后，我们的系统在子任务A（等级11）中达到92.9％的精度，子任务B（等级9）的精度为89.7％，子任务中的Blebleu得分为12.9（等级8）（等级8）
This paper describes our system submitted to task 4 of SemEval 2020: Commonsense Validation and Explanation (ComVE) which consists of three sub-tasks. The task is to directly validate the given sentence whether or not it makes sense and require the model to explain it. Based on BERTarchitecture with a multi-task setting, we propose an effective and interpretable "Explain, Reason and Predict" (ERP) system to solve the three sub-tasks about commonsense: (a) Validation, (b)Reasoning, and (c) Explanation. Inspired by cognitive studies of common sense, our system first generates a reason or understanding of the sentences and then chooses which one statement makes sense, which is achieved by multi-task learning. During the post-evaluation, our system has reached 92.9% accuracy in subtask A (rank 11), 89.7% accuracy in subtask B (rank 9), andBLEU score of 12.9 in subtask C (rank 8)
