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Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification

Ashwin Geet d'Sa 1 Irina Illina 1 Dominique Fohr 1 Dietrich Klakow 2 Dana Ruiter 2
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Research on hate speech classification has received increased attention. In real-life scenarios , a small amount of labeled hate speech data is available to train a reliable classifier. Semi-supervised learning takes advantage of a small amount of labeled data and a large amount of unlabeled data. In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification. The quality of labeling the unla-beled set depends on the input representations. In this work, we show that pre-trained representations are label agnostic, and when used with label propagation yield poor results. Neu-ral network-based fine-tuning can be adopted to learn task-specific representations using a small amount of labeled data. We show that fully fine-tuned representations may not always be the best representations for the label propagation and intermediate representations may perform better in a semi-supervised setup.
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https://hal.inria.fr/hal-02964065
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Submitted on : Monday, October 12, 2020 - 10:15:31 AM
Last modification on : Tuesday, October 13, 2020 - 3:37:50 AM

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Ashwin Geet d'Sa, Irina Illina, Dominique Fohr, Dietrich Klakow, Dana Ruiter. Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification. Insights from Negative Results Workshop, EMNLP 2020, Nov 2020, Punta Cana, Dominican Republic. ⟨hal-02964065⟩

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