论文标题
仇恨语音识别的基于功能提取的模型
A Feature Extraction based Model for Hate Speech Identification
论文作者
论文摘要
在线发现仇恨言论已成为一项重要任务，因为诸如伤害，淫秽和侮辱性内容之类的进攻性语言会损害边缘化的人或团体。本文介绍了柏林团队的实验，并在仇恨言论和进攻性内容识别的任务1a和1b的结果中，以印欧语言2021。在整个竞争中，各自的子任务评估了不同自然语言处理模型的成功。我们基于单词和角色级别的复发神经网络测试了不同的模型，并根据竞争对手提供的数据集对BERT进行了转移学习方法。在用于实验的测试模型中，基于转移学习的模型在两个子任务中都取得了最佳结果。
The detection of hate speech online has become an important task, as offensive language such as hurtful, obscene and insulting content can harm marginalized people or groups. This paper presents TU Berlin team experiments and results on the task 1A and 1B of the shared task on hate speech and offensive content identification in Indo-European languages 2021. The success of different Natural Language Processing models is evaluated for the respective subtasks throughout the competition. We tested different models based on recurrent neural networks in word and character levels and transfer learning approaches based on Bert on the provided dataset by the competition. Among the tested models that have been used for the experiments, the transfer learning-based models achieved the best results in both subtasks.
