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The paper introduces PolERo, a dataset of 3,574 annotated question-answer pairs from Romanian presidential transcripts, to study political evasion. It evaluates various NLP classification models and examines cross-lingual transfer, finding that fine-tuned encoders are competitive and ambivalent evasion categories are challenging.