  This paper introduces our systems for all three subtasks of SemEval-2021 Task
4: Reading Comprehension of Abstract Meaning. To help our model better
represent and understand abstract concepts in natural language, we well-design
many simple and effective approaches adapted to the backbone model (RoBERTa).
Specifically, we formalize the subtasks into the multiple-choice question
answering format and add special tokens to abstract concepts, then, the final
prediction of question answering is considered as the result of subtasks.
Additionally, we employ many finetuning tricks to improve the performance.
Experimental results show that our approaches achieve significant performance
compared with the baseline systems. Our approaches achieve eighth rank on
subtask-1 and tenth rank on subtask-2.
