基于上下文嵌入的鲁棒仇恨语音检测

J. Hoffmann, Udo Kruschwitz
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引用次数: 5

摘要

我们描述了我们解决EVALITA 2020仇恨语音检测(HaSpeeDe2)挑战的任务A的方法。我们提交了两个基于上下文嵌入的运行-我们选择上下文嵌入是因为它们在解决广泛的NLP问题方面的有效性。对于我们的基线运行,我们使用堆叠嵌入作为线性支持向量机的特征。我们的第二次运行是三个支持向量机的简单集成方法,具有多数投票。这两种方法的性能都大大超过了官方基线,特别是集成分类器在不同类型的测试数据上表现出了强大的性能:在新闻标题行中获得第6名(27次运行),在Twitter feed中获得第10名(27次运行)。
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UR NLP @ HaSpeeDe 2 at EVALITA 2020: Towards Robust Hate Speech Detection with Contextual Embeddings
We describe our approach to addressTask A of the EVALITA 2020 Hate SpeechDetection (HaSpeeDe2) challenge.Wesubmitted two runs that are both based oncontextual embeddings – which we hadchosen due to their effectiveness in solvinga wide range of NLP problems. For ourbaseline run we use stacked embeddingsthat serve as features in a linear SVM. Oursecond run is a simple ensemble approachof three SVMs with majority voting. Bothapproaches outperform the official base-lines by a large margin, and the ensembleclassifier in particular demonstrates robustperformance on different types of test datacoming 6th (out of 27 runs) for news head-lines and 10th (out of 27) for Twitter feeds.
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DIACR-Ita @ EVALITA2020: Overview of the EVALITA2020 Diachronic Lexical Semantics (DIACR-Ita) Task QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian (short paper) By1510 @ HaSpeeDe 2: Identification of Hate Speech for Italian Language in Social Media Data (short paper) HaSpeeDe 2 @ EVALITA2020: Overview of the EVALITA 2020 Hate Speech Detection Task KIPoS @ EVALITA2020: Overview of the Task on KIParla Part of Speech Tagging
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