Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification
Autor: | Dietrich Klakow, Irina Illina, Dana Ruiter, Ashwin Geet D'Sa, Dominique Fohr |
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Přispěvatelé: | Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Saarland University [Saarbrücken], GRID5000 |
Rok vydání: | 2020 |
Předmět: |
ComputingMethodologies_PATTERNRECOGNITION
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Computer science Speech recognition 0202 electrical engineering electronic engineering information engineering Labeled data [INFO]Computer Science [cs] 020206 networking & telecommunications 02 engineering and technology Semi-supervised learning Speech classification Classifier (UML) Label propagation |
Zdroj: | Insights Insights from Negative Results Workshop, EMNLP 2020 Insights from Negative Results Workshop, EMNLP 2020, Nov 2020, Punta Cana, Dominican Republic |
DOI: | 10.18653/v1/2020.insights-1.8 |
Popis: | International audience; 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. |
Databáze: | OpenAIRE |
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