任务4:使用微调语言模型、BERT+BiGRU和集成模型的居高临下的语言检测

Ali Edalat, Yadollah Yaghoobzadeh, B. Bahrak
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摘要

本文介绍了AliEdalat团队在SemEval-2022任务4:居高临下的语言(PCL)检测中的方法和结果。这项任务旨在检测文本中是否存在PCL和PCL类别,以防止对弱势群体的进一步歧视。我们使用三种基本模型的集合来检测PCL的存在:微调bigbird,微调mpnet和BERT+BiGRU。由于过拟合,集成模型的性能比基线差,f1得分为0.3031。我们提供了另一种解决方案来解决提交模型的问题。我们分别考虑不同类别的PCL。为了检测每一类PCL,我们就像一个PCL检测器。而不是BERT+BiGRU,我们在模型中使用微调roberta。在PCL类别检测中,我们的模型优于基线模型,f1得分为0.2531。我们还提出了检测两类PCL的新模型,其性能优于所提交的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AliEdalat at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Fine-tuned Language Models, BERT+BiGRU, and Ensemble Models
This paper presents the AliEdalat team’s methodology and results in SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection. This task aims to detect the presence of PCL and PCL categories in text in order to prevent further discrimination against vulnerable communities. We use an ensemble of three basic models to detect the presence of PCL: fine-tuned bigbird, fine-tuned mpnet, and BERT+BiGRU. The ensemble model performs worse than the baseline due to overfitting and achieves an F1-score of 0.3031. We offer another solution to resolve the submitted model’s problem. We consider the different categories of PCL separately. To detect each category of PCL, we act like a PCL detector. Instead of BERT+BiGRU, we use fine-tuned roberta in the models. In PCL category detection, our model outperforms the baseline model and achieves an F1-score of 0.2531. We also present new models for detecting two categories of PCL that outperform the submitted models.
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