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This paper investigates computational pun translation as a process of discovery, exploration, and selection, using graph-based affordance retrieval and multi-evaluator ranking. The authors show that successful pun translation relies on finding new sound-meaning collisions in the target language rather than preserving source words.
This paper presents an overview of the second edition of the TalentCLEF challenge at CLEF 2026, which includes tasks on job-person matching and job-skill matching in English and Spanish, attracting over 400 submissions.