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The Smatch metric is a popular method for evaluating graph distances, as isnecessary, for instance, to assess the performance of semantic graph parsingsystems. However, we observe some issues in the metric that jeopardizemeaningful evaluation. E.g., opaque pre-processing choices can affect results,and current graph-alignment solvers do not provide us with upper-bounds.Without upper-bounds, however, fair evaluation is not guaranteed. Furthermore,adaptions of Smatch for extended tasks (e.g., fine-grained semantic similarity)are spread out, and lack a unifying framework.  For better inspection, we divide the metric into three modules:pre-processing, alignment, and scoring. Examining each module, we specify itsgoals and diagnose potential issues, for which we discuss and test mitigationstrategies. For pre-processing, we show how to fully conform to annotationguidelines that allow structurally deviating but valid graphs. For safer andenhanced alignment, we show the feasibility of optimal alignment in a standardevaluation setup, and develop a lossless graph compression method that shrinksthe search space and significantly increases efficiency. For improved scoring,we propose standardized and extended metric calculation of fine-grainedsub-graph meaning aspects. Our code is available athttps://github.com/flipz357/smatchpp

SMATCH++: Standardized and Extended Evaluation of Semantic Graphs