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)

本文提出了一种基于 BERT 模型架构和多任务学习的 “解释，推理和预测” 系统，用于解决 SemEval 2020（通识验证和解释）的子任务，包括通识的验证、推理和解释。该系统受通识认知研究启发，首先对句子的理解和推理做出解释，然后通过多任务学习来选择一条制约语句是否合理，最终在评估中取得了不错的效果。