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Updated: Jul 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Label modification and bootstrapping for zero-shot cross-lingual hate speech detection.

Irina Bigoulaeva1, Viktor Hangya2, Iryna Gurevych1

  • 1Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany.

Language Resources and Evaluation
|November 29, 2023
PubMed
Summary

This study addresses hate speech detection in low-resource languages using cross-lingual transfer learning. Techniques like word embeddings and data balancing improve model performance for multilingual online content moderation.

Keywords:
BERTCNNClass imbalanceCross-lingual transfer learningHate speechLSTM

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Machine Learning

Background:

  • Hate speech detection is vital for online safety, especially across diverse languages.
  • Limited labeled data in low-resource languages hinders effective hate speech detection systems.
  • Inconsistent labeling and definitions across datasets complicate cross-lingual transfer learning.

Purpose of the Study:

  • To develop effective hate speech detection for low-resource languages via cross-lingual transfer learning.
  • To address challenges posed by data scarcity and label inconsistencies in multilingual hate speech detection.
  • To improve model performance by incorporating unlabeled data and handling label imbalance.

Main Methods:

  • Leveraging cross-lingual word embeddings for training models on source languages and applying them to target languages.
  • Utilizing an ensemble of model architectures for bootstrapping labels from unlabeled target language data.
  • Implementing data undersampling and oversampling techniques to mitigate label imbalance.

Main Results:

  • Achieved good performance in hate speech detection for low-resource languages using cross-lingual transfer learning.
  • Demonstrated effectiveness of incorporating unlabeled data through label bootstrapping.
  • Showed significant improvements in model performance by addressing label imbalance with sampling techniques.

Conclusions:

  • Cross-lingual transfer learning is a viable approach for hate speech detection in low-resource settings.
  • Addressing data scarcity and label issues is crucial for robust multilingual hate speech detection.
  • Data balancing techniques effectively enhance model performance in imbalanced hate speech datasets.