Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification - Archive ouverte HAL Access content directly
Conference Papers Year :

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

(1) , (1) , (1) , (2) , (2)
1
2

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.
Fichier principal
Vignette du fichier
EMNLP_2020_Insights_workshop_CRC.pdf (1008.42 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02964065 , version 1 (12-10-2020)

Identifiers

  • HAL Id : hal-02964065 , version 1

Cite

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⟩
176 View
343 Download

Share

Gmail Facebook Twitter LinkedIn More