  Recently proposed BERT-based evaluation metrics for text generation perform
well on standard benchmarks but are vulnerable to adversarial attacks, e.g.,
relating to information correctness. We argue that this stems (in part) from
the fact that they are models of semantic similarity. In contrast, we develop
evaluation metrics based on Natural Language Inference (NLI), which we deem a
more appropriate modeling. We design a preference-based adversarial attack
framework and show that our NLI based metrics are much more robust to the
attacks than the recent BERT-based metrics. On standard benchmarks, our NLI
based metrics outperform existing summarization metrics, but perform below SOTA
MT metrics. However, when combining existing metrics with our NLI metrics, we
obtain both higher adversarial robustness (15%-30%) and higher quality metrics
as measured on standard benchmarks (+5% to 30%).
