论文标题
Taygete在Semeval-2022任务4：基于罗伯塔的模型，用于检测光顾和屈服语言
Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language
论文作者
论文摘要
这项工作描述了不同模型的发展，以在新闻文章的摘录中检测出光顾和屈服于2022年竞赛（Task-4）的一部分。这项工作探索了基于预先训练的Roberta语言模型以及LSTM和CNN层的不同模型。最佳模型获得了15 $^{th} $排名，子任务为0.5924，在子任务-B中为12 $^{th} $，宏F1分数为0.3763。
This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The best models achieved 15$^{th}$ rank with an F1-score of 0.5924 for subtask-A and 12$^{th}$ in subtask-B with a macro-F1 score of 0.3763.
