Imagine trying to teach a computer to understand not just *what* words are in a sentence, but also *who did what to whom*. That's the challenge of Semantic Role Labeling (SRL), a crucial task for making sense of language. SRL powers many applications we use every day, from accurate machine translation to insightful question answering. However, training AI for SRL in multiple languages is tricky due to the lack of labeled data in many languages. Plus, existing methods can hallucinate, or invent, roles that aren't actually there, leading to inaccurate interpretations. A new research paper introduces DAHRS (Divergence-Aware Hallucination-Remediated SRL Projection), a clever technique to address these issues. DAHRS tackles the problem of "hallucinations" in cross-lingual SRL by understanding the quirks of translation. For example, a single English verb might translate to multiple words in another language, or the word order might change significantly. These "divergences" can trip up current AI models, leading them to assign incorrect roles. DAHRS cleverly corrects these misalignments *before* projecting the semantic roles, improving the accuracy of the entire process. It also handles phrases, not just individual words, which offers an even richer understanding. The results? DAHRS outperforms existing methods in English-French and English-Spanish translation, achieving higher accuracy without needing mountains of manually labeled data. What's even more exciting is that DAHRS offers a peek under the hood. Its decisions are transparent and visualized, allowing researchers to understand *why* a specific role was assigned. This explainability is a big step forward in making AI for language understanding more trustworthy and reliable. The future of DAHRS looks bright, with the potential to unlock SRL in low-resource languages like Tagalog. This opens doors for better communication and understanding across a much wider range of languages, making the world a little bit smaller, one translated sentence at a time.