Hate, derogatory, and offensive speech remains a persistent challenge in online platforms and public discourse. While automated detection systems are widely used, most focus on censorship or removal, raising concerns for transparency and freedom of expression, and limiting opportunities to explain why content is harmful. To address these issues, explanatory approaches have emerged as a promising solution, aiming to make hate speech detection more transparent, accountable, and informative. In this paper, we present a hybrid approach that combines Large Language Models (LLMs) with three newly created and curated vocabularies to detect and explain hate speech in English, French, and Greek. Our system captures both inherently derogatory expressions tied to identity characteristics and direct group-targeted content through two complementary pipelines: one that detects and disambiguates problematic terms using the curated vocabularies, and one that leverages LLMs as context-aware evaluators of group-targeting content. The outputs are fused into grounded explanations that clarify why content is flagged. Human evaluation shows that our hybrid approach is accurate, with high-quality explanations, outperforming LLM-only baselines.

The AI4Culture project, funded by the European Commission (2023-2025), developed a platform (https://ai4culture.eu) to educate cultural heritage (CH) professionals in AI technologies. Acting as an online capacity building hub, the platform describes openly labeled data sets and deployable and reusable tools applying AI technologies in tasks relevant to the CH sector. It also offers tutorials for tools and recipes for the combination of tools. In addition, the platform allows users to contribute their own resources. The resources described by project partners involve applications for optical or handwritten character recognition (OCR, HTR), generation and validation of subtitles, machine translation, image analysis, and semantic linking. The partners customized various tools to enhance the usability of interfaces and components. Here, we zoom in on the use case of correcting OCR/HTR output using various means (such as an unstructured manual transcription) to facilitate multilingual accessibility and create structured ground truth (text lines with image coordinates).

Europeana Translate is a project funded under the Connecting European Facility with the objective to take advantage of state-of-the-art machine translation in order to increase the multilinguality of resources in the cultural heritage domain