Before revision:  The second metric, the Entailing Entity Match score, augments the first metric by introducing a natural language inference model into the entity match process to encourage consistent generations that can be entailed from the references. To achieve this, we also developed an in-domain NLI model via weak supervision to improve its performance on radiology text .
After revision:  The second metric, the Entailing Entity Match score, augments the first metric by introducing a natural language inference to encourage these entities to be described in inferentially consistent ways. We combine these with the novel use of an existing semantic equivalence metric (BERTScore) .