论文标题
iie-nlp-nut在Semeval-2020任务4：引导PLM带有及时的模板重建策略
IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE
论文作者
论文摘要
本文介绍了Semeval任务的前两个子任务的系统4：常识验证和解释。为了阐明判断和注入对比信息进行选择的意图，我们提出了使用及时模板的输入重建策略。具体而言，我们将子任务正式化为多项选择的问题答案格式，并使用及时模板构造输入，然后，将问题回答的最终预测视为子任务的结果。实验结果表明，与基线系统相比，我们的方法实现了显着的性能。我们的方法在前两个子任务的两个官方测试集上确保了第三个等级，精度为96.4和精度为94.3。
This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation. To clarify the intention for judgment and inject contrastive information for selection, we propose the input reconstruction strategy with prompt templates. Specifically, we formalize the subtasks into the multiple-choice question answering format and construct the input with the prompt templates, then, the final prediction of question answering is considered as the result of subtasks. Experimental results show that our approaches achieve significant performance compared with the baseline systems. Our approaches secure the third rank on both official test sets of the first two subtasks with an accuracy of 96.4 and an accuracy of 94.3 respectively.
