Metric to evaluate words generated by Neural Network
Maybe somebody will have a better idea but the default method would be to generate a set of names, then ask a few annotators to label them as good or bad (possibly scoring them from 1 very bad to 5 very good), and finally train a supervised model to recognize the good from the bad ones. This approach would also give you the opportunity to check the inter-annotator agreement, i.e. assess how subjective the choice good vs. bad is. In general a subjective task is difficult (or impossible) to do with an unsupervised metric.