在SemEval-2020任务12:攻击性语言识别探索迁移学习模型

Flor Miriam Plaza del Arco, M. Dolores Molina González, Alfonso Ureña-López, Maite Martin
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引用次数: 4

摘要

本文描述了西奈团队在任务12:进攻2:社交媒体中的多语言攻击性语言识别中的参与情况。特别是在英语的子任务A的参与,其中包括识别推文是攻击性的还是非攻击性的。我们根据社交媒体上使用的语言特征对数据集进行预处理。然后,我们从组织者提供的训练集中选择一个小集合,并对不同的基于transformer的模型进行微调,以测试它们的有效性。我们的团队使用XLNet模型在Subtask-A的85名参与者中排名第20位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SINAI at SemEval-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models
This paper describes the participation of SINAI team at Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media. In particular, the participation in Sub-task A in English which consists of identifying tweets as offensive or not offensive. We preprocess the dataset according to the language characteristics used on social media. Then, we select a small set from the training set provided by the organizers and fine-tune different Transformerbased models in order to test their effectiveness. Our team ranks 20th out of 85 participants in Subtask-A using the XLNet model.
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