论文标题
XU在Semeval-2022任务4：前伯特神经网络方法与伯伯特·罗伯塔邮政在罗伯塔邮政
Xu at SemEval-2022 Task 4: Pre-BERT Neural Network Methods vs Post-BERT RoBERTa Approach for Patronizing and Condescending Language Detection
论文作者
论文摘要
本文描述了我参与Semeval-2022任务4：光顾和屈服于语言检测。我参与了这两个子任务：光顾和屈服语言（PCL）识别以及光顾和屈服于语言分类，主要重点放在子任务1中。实验比较基于后的前伯特神经网络（NN）系统与后遗产后的语言模型Roberta相比。这项研究发现，与审计的语言模型相比，实验中基于NN的系统在任务上的表现较差。在子任务1中，最佳的罗伯塔系统在78个团队中排名第26（F1得分：54.64），在子任务2中的49支球队中有26个（F1得分：30.03）中有26个。
This paper describes my participation in the SemEval-2022 Task 4: Patronizing and Condescending Language Detection. I participate in both subtasks: Patronizing and Condescending Language (PCL) Identification and Patronizing and Condescending Language Categorization, with the main focus put on subtask 1. The experiments compare pre-BERT neural network (NN) based systems against post-BERT pretrained language model RoBERTa. This research finds NN-based systems in the experiments perform worse on the task compared to the pretrained language models. The top-performing RoBERTa system is ranked 26 out of 78 teams (F1-score: 54.64) in subtask 1, and 23 out of 49 teams (F1-score: 30.03) in subtask 2.
