Cyberbullying (CB) involves complex relational dynamics that are often oversimplified as a binary classification task. Existing youth-focused CB datasets rely on scripted role-play, lacking conversational realism and ethical youth involvement, with little or no evaluation of their social plausibility. To address this, we introduce a youth-in-the-loop dataset “BullyBench” developed by adolescents (ages 15–16) through an ethical co-research framework. We introduce a structured intrinsic quality evaluation with experts-in-the-loop (social scientists, psychologists, and content moderators) for assessing realism, relevance, and coherence in youth CB data. Additionally, we perform extrinsic baseline evaluation of this dataset by benchmarking encoder- and decoder-only language models for multi-class CB role classification for future research. A three-stage annotation process by young adults refines the dataset into a gold-standard test benchmark, a high-quality resource grounded in minors’ lived experiences of CB detection. Code and data are available for review

In the last few years the European Parliament has witnessed a significant increase in translation demand. Although Translation Memory (TM) tools, terminology databases and bilingual concordancers have provided significant leverage in terms of quality and productivity the European Parliament is in need for advanced language technology to keep facing successfully the challenge of multilingualism. This paper describes an ongoing large-scale machine translation post-editing evaluation campaign the purpose of which is to estimate the business benefits from the use of machine translation for the European Parliament. This paper focuses mainly on the design, the methodology and the tools used by the evaluators but it also presents some preliminary results for the following language pairs: Polish-English, Danish-English, Lithuanian-English, English-German and English-French.