任务10:基于变形器和任务自适应预训练的可解释的性别歧视检测

Hadi Mahmoudi
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引用次数: 0

摘要

本文描述了我们在SemEval-2023任务10上的系统:可解释的在线性别歧视检测(EDOS)。本工作旨在设计一个自动系统来检测和分类网络空间中的性别歧视内容。我们提出了一组具有任务自适应预训练和集成学习的基于变压器的预训练模型。该系统的主要贡献包括分析不同的基于变压器的预训练模型的性能并将这些模型组合起来,以及提供一种使用大量未标记数据进行模型自适应预训练的有效方法。我们还探讨了其他几种策略。在测试数据集上,我们的系统在子任务A、B和C上分别达到了83%、64%和47%的f1得分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IUST_NLP at SemEval-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining
This paper describes our system on SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This work aims to design an automatic system for detecting and classifying sexist content in online spaces. We propose a set of transformer-based pre-trained models with task-adaptive pretraining and ensemble learning. The main contributions of our system include analyzing the performance of different transformer-based pre-trained models and combining these models, as well as providing an efficient method using large amounts of unlabeled data for model adaptive pretraining. We have also explored several other strategies. On the test dataset, our system achieves F1-scores of 83%, 64%, and 47% on subtasks A, B, and C, respectively.
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