This paper describes our winning system on SemEval 2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts. A replaced token detection pre-trained model is utilized with minorly different task-specific heads for SubTask-A: Multi-class Classification and SubTask-B: Ranking. Incorporating a pattern-aware ensemble method, our system achieves a 68.90% accuracy score and 0.8070 spearman's rank correlation score surpassing the 2nd place with a large margin by 2.7 and 2.2 percent points for SubTask-A and SubTask-B, respectively. Our approach is simple and easy to implement, and we conducted ablation studies and qualitative and quantitative analyses for the working strategies used in our system.
翻译:本文描述了我们在SemEval 2022任务7:确定教学文本中隐含和未明确规定的词句的显著澄清的优胜系统:在教学文本中,用一种替代的象征性检测预培训模式,使用的任务类别与任务类别稍有不同,用于亚任务A:多级分类和亚任务B:排名。采用了有模式组合法,我们的系统实现了68.90%的准确度分和0.8070矛手的排名比第二位高出2.7%和2.2%的比值,对亚任务A和亚任务B,我们的方法简单易行,我们为本系统使用的工作战略进行了消缩研究以及定性和定量分析。