斯坦福MLab在SemEval-2023任务10：探索基于GloVe和Transformer的方法用于在线性别歧视的可解释检测
Stanford MLab at SemEval-2023 Task 10: Exploring GloVe- and Transformer-Based Methods for the Explainable Detection of Online Sexism
AI总结：
本研究探索了多种模型（包括GloVe、BERT、RoBERTa、DeBERTa及集成方法）用于在线性别歧视的可解释检测，通过三个分类任务预测性别歧视并分类子类别，发现预训练Transformer显著提升性能，集成和混合增强了鲁棒性。
摘要：
在本文中，我们讨论了在SemEval-2023任务10“迈向在线性别歧视的可解释检测”中应用的方法。给定输入文本，我们执行三个分类任务来预测文本是否具有性别歧视，并将性别歧视文本分类为子类别，以提供关于文本为何具有性别歧视的额外解释。我们探索了多种不同类型的模型，包括作为基线方法的GloVe嵌入，基于Transformer的深度学习模型如BERT、RoBERTa和DeBERTa，集成模型以及模型混合。我们探索了各种数据清洗和增强方法来提高模型性能。预训练Transformer模型带来了显著的性能提升，集成和混合在F1分数上略微提高了鲁棒性。
英文摘要：
In this paper, we discuss the methods we applied at SemEval-2023 Task 10: Towards the Explainable Detection of Online Sexism. Given an input text, we perform three classification tasks to predict whether the text is sexist and classify the sexist text into subcategories in order to provide an additional explanation as to why the text is sexist. We explored many different types of models, including GloVe embeddings as the baseline approach, transformer-based deep learning models like BERT, RoBERTa, and DeBERTa, ensemble models, and model blending. We explored various data cleaning and augmentation methods to improve model performance. Pre-training transformer models yielded significant improvements in performance, and ensembles and blending slightly improved robustness in the F1 score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
