  Evaluation metrics are a key ingredient for progress of text generation
systems. In recent years, several BERT-based evaluation metrics have been
proposed (including BERTScore, MoverScore, BLEURT, etc.) which correlate much
better with human assessment of text generation quality than BLEU or ROUGE,
invented two decades ago. However, little is known what these metrics, which
are based on black-box language model representations, actually capture (it is
typically assumed they model semantic similarity). In this work, we use a
simple regression based global explainability technique to disentangle metric
scores along linguistic factors, including semantics, syntax, morphology, and
lexical overlap. We show that the different metrics capture all aspects to some
degree, but that they are all substantially sensitive to lexical overlap, just
like BLEU and ROUGE. This exposes limitations of these novelly proposed
metrics, which we also highlight in an adversarial test scenario.
