Transferring Knowledge via Neighborhood-Aware Optimal Transport for Low-Resource Hate Speech Detection

• Main Authors: Bose, Tulika, Illina, Irina, Fohr, Dominique
• Format: Journal Article
• Language: English
• Published: 17-10-2022
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.2210.09340

The concerning rise of hateful content on online platforms has increased the
attention towards automatic hate speech detection, commonly formulated as a
supervised classification task. State-of-the-art deep learning-based approaches
usually require a substantial amount of labeled resources for training.
However, annotating hate speech resources is expensive, time-consuming, and
often harmful to the annotators. This creates a pressing need to transfer
knowledge from the existing labeled resources to low-resource hate speech
corpora with the goal of improving system performance. For this,
neighborhood-based frameworks have been shown to be effective. However, they
have limited flexibility. In our paper, we propose a novel training strategy
that allows flexible modeling of the relative proximity of neighbors retrieved
from a resource-rich corpus to learn the amount of transfer. In particular, we
incorporate neighborhood information with Optimal Transport, which permits
exploiting the geometry of the data embedding space. By aligning the joint
embedding and label distributions of neighbors, we demonstrate substantial
improvements over strong baselines, in low-resource scenarios, on different
publicly available hate speech corpora.