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Semantic role labeling (SRL) identifies the predicate-argument structure in asentence. This task is usually accomplished in four steps: predicateidentification, predicate sense disambiguation, argument identification, andargument classification. Errors introduced at one step propagate to latersteps. Unfortunately, the existing SRL evaluation scripts do not consider thefull effect of this error propagation aspect. They either evaluate argumentsindependent of predicate sense (CoNLL09) or do not evaluate predicate sense atall (CoNLL05), yielding an inaccurate SRL model performance on the argumentclassification task. In this paper, we address key practical issues withexisting evaluation scripts and propose a more strict SRL evaluation metricPriMeSRL. We observe that by employing PriMeSRL, the quality evaluation of allSoTA SRL models drops significantly, and their relative rankings also change.We also show that PriMeSRLsuccessfully penalizes actual failures in SoTA SRLmodels.

PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation