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Paraphrasing is rooted in semantics, which makes evaluating paraphrase generation systems hard. Current paraphrase generators are typically evaluated using borrowed metrics from adjacent text-to-text tasks, like machine translation or text summarization. These metrics tend to have ties to the surface form of the reference text. This is not ideal for paraphrases as we typically want variation in the lexicon while persisting semantics. To address this problem, and inspired by learned similarity evaluation on plain text, we propose PAM, a Paraphrase AMR-Centric Evaluation Metric. This metric uses AMR graphs extracted from the input text, which consist of semantic structures agnostic to the text surface form, making the resulting evaluation metric more robust to variations in syntax or lexicon. Additionally, we evaluated PAM on different semantic textual similarity datasets and found that it improves the correlations with human semantic scores when compared to other AMR-based metrics.