论文标题
Nowruz在Semeval-2022任务7：通过变压器和序数回归解决披肩测试
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression
论文作者
论文摘要
本文概述了使用Nowruz参与Semeval 2022任务的系统7确定对两个子任务A和B的合理澄清。使用预训练的变压器作为骨干，该模型针对了与最佳填充任务的上下文，使用预先训练的变压器作为骨架，将模型定为多任务分类和排名。 该系统采用了两个序数回归组件的组合来解决此任务，以在多任务学习方案中解决此任务。根据共享任务的官方排行榜，该系统在排名中排名第五，在21个参与团队中的分类子任务中排名第七。通过其他实验，这些模型已得到进一步优化。
This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases for both subtasks A and B. Using a pre-trained transformer as a backbone, the model targeted the task of multi-task classification and ranking in the context of finding the best fillers for a cloze task related to instructional texts on the website Wikihow. The system employed a combination of two ordinal regression components to tackle this task in a multi-task learning scenario. According to the official leaderboard of the shared task, this system was ranked 5th in the ranking and 7th in the classification subtasks out of 21 participating teams. With additional experiments, the models have since been further optimised.
