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Domain Similarity Impact on Multilingual Hate Speech Detection Generalization in Zero-Shot Cross-Lingual Transfer

Domaine:

natural language processing

Type de record:

paper
Créateur:
Ass
Éditeur:
Zenodo
Hôte:avatar
Automatic detection of abusive online content such as hate speech, offensive language, threats, etc. has become prevalent in social media, with multiple efforts dedicated to detecting this phenomenon in English. However, detecting hatred and abuse in low-resource languages is a non-trivial challenge. The lack of sufficient labeled data in low-resource languages and inconsistent generalization ability of transformer-based multilingual pre-trained language models for typologically diverse languages make these models inefficient in some cases. We propose a meta learning-based approach to study th Research goal: How does the domain similarity between auxiliary tasks and target hate speech detection influence the generalization performance of multilingual models in zero-shot cross-lingual transfer, measured by precision and recall on low-resource languages in the XTREME-R benchmark? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.2/10.

Visit

doi.orgzenodo.org

Tasks

hate speech detectiontext classificationtransfer learning

Tags

domainsimilarityauxiliarytaskstargethatespeechdetection

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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