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The ability to compare the semantic similarity between text corpora isimportant in a variety of natural language processing applications. However,standard methods for evaluating these metrics have yet to be established. Wepropose a set of automatic and interpretable measures for assessing thecharacteristics of corpus-level semantic similarity metrics, allowing sensiblecomparison of their behavior. We demonstrate the effectiveness of ourevaluation measures in capturing fundamental characteristics by evaluating themon a collection of classical and state-of-the-art metrics. Our measuresrevealed that recently-developed metrics are becoming better in identifyingsemantic distributional mismatch while classical metrics are more sensitive toperturbations in the surface text levels.

Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora