Work

Towards Safer Hebrew Communication: A Dataset for Offensive Language Detoxification

Year: 2025

Type: conference-paper

Abstract: Text detoxification is the task of transforming offensive or toxic content into a non-offensive form while preserving the original meaning.Despite increasing research interest in detoxification across various languages, no resources or benchmarks exist for Hebrew, a Semitic language with unique morphological, syntactic, and cultural characteristics.This paper introduces HeDetox, the first annotated dataset for text detoxification in Hebrew.HeDetox contains 600 sentence pairs, each consisting of an offensive source text and a non-offensive text rewritten with LLM and human intervention.We present a detailed dataset analysis and evaluation showing that the dataset benefits offensive language detection.HeDetox offers a foundational resource for Hebrew natural language processing, advancing research in offensive language mitigation and controllable text generation.

Source: International conference Recent advances in natural language processing

DOI: 10.26615/978-954-452-098-4-149

Authors: Natalia Vanetik, Lior Liberov, Marina Litvak, Chaya Liebeskind

Institutions: Sami Shamoon College of Engineering, Jerusalem College of Technology

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W7125157196

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