Building robust natural language understanding systems will require a clear\ncharacterization of whether and how various linguistic meaning representations\ncomplement each other. To perform a systematic comparative analysis, we\nevaluate the mapping between meaning representations from different frameworks\nusing two complementary methods: (i) a rule-based converter, and (ii) a\nsupervised delexicalized parser that parses to one framework using only\ninformation from the other as features. We apply these methods to convert the\nSTREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a\ngraph-structured full-sentence meaning representation). Both methods yield\nsurprisingly accurate target representations, close to fully supervised UCCA\nparser quality---indicating that UCCA annotations are partially redundant with\nSTREUSLE annotations. Despite this substantial convergence between frameworks,\nwe find several important areas of divergence.\n