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.

本文介绍了我们针对SemEval-2021任务4的三个子任务的系统：抽象意义阅读理解的所有三个子任务。为了帮助我们的模型更好地表示和理解自然语言中的抽象概念，我们设计了很多简单而有效的方法，适应于基础模型（RoBERTa）, 并采用多项选择问题回答的格式以及添加特殊标记到抽象概念，将问题回答的最终预测视为子任务的结果。此外，我们采用了许多微调技巧来提高性能。实验结果表明，我们的方法相对于基线系统表现出显著的性能差异， 在子任务1上获得第8名，在子任务2上获得第10名。

IIE-NLP-Eyas在SemEval-2021任务4中：通过特殊标记、重新排序、孪生编码器和回译增强PLM以用于ReCAM