Paper ID: 2211.16259

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

George Kour, Samuel Ackerman, Orna Raz, Eitan Farchi, Boaz Carmeli, Ateret Anaby-Tavor

The ability to compare the semantic similarity between text corpora is important in a variety of natural language processing applications. However, standard methods for evaluating these metrics have yet to be established. We propose a set of automatic and interpretable measures for assessing the characteristics of corpus-level semantic similarity metrics, allowing sensible comparison of their behavior. We demonstrate the effectiveness of our evaluation measures in capturing fundamental characteristics by evaluating them on a collection of classical and state-of-the-art metrics. Our measures revealed that recently-developed metrics are becoming better in identifying semantic distributional mismatch while classical metrics are more sensitive to perturbations in the surface text levels.

Submitted: Nov 29, 2022