论文标题
ML_LTU在Semeval-2022任务4：T5识别光顾和屈服语言
ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language
论文作者
论文摘要
本文介绍了Semeval-2022任务4：光顾和屈服语言（PCL）检测的LTU机器学习组使用的系统。我们的系统包括对经过验证的文本到文本转移变压器（T5）的填充，并创新地降低了其阶层的预测。本文的主要贡献是1）我们使用的T5模型的实现细节的描述，2）分析该任务中该模型的成功和挣扎的分析，以及3）除官方提交以外的消融研究以确定数据拆分的相对重要性。我们的模型在官方测试集中达到0.5452的F1得分。
This paper describes the system used by the Machine Learning Group of LTU in subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection. Our system consists of finetuning a pretrained Text-to-Text-Transfer Transformer (T5) and innovatively reducing its out-of-class predictions. The main contributions of this paper are 1) the description of the implementation details of the T5 model we used, 2) analysis of the successes & struggles of the model in this task, and 3) ablation studies beyond the official submission to ascertain the relative importance of data split. Our model achieves an F1 score of 0.5452 on the official test set.
