Metric to evaluate words generated by Neural Network I have this task at hand and I would be grateful for some directions. Perfectly not the final solution as I would like to do it myself. 

Let's say I need to create new fruit names based on existing ones. 

I made it using textgenrnn library. 

Now I have a list of potential new names. 
I would like to build a metric which would calculate that e.g.:


(Fabic, Alis, Brooty, Morange etc) are potentially good fruit name
(Ssae, Sriew, Adeoie, Seeea) are potential bad fruit names. 


Is there any list of metrics for word generating?

As of now, I found following resources:


Evaluating Generative Models for Text Generation https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2737434.pdf
Character-Level Language Modeling with Hierarchical Recurrent Neural
Networks http://150.162.46.34:8080/icassp2017/pdfs/0005720.pdf
Graves A. Generating Sequences With Recurrent Neural Networks
https://arxiv.org/pdf/1308.0850.pdf
Subword language modeling with neural networks.
http://www.fit.vutbr.cz/~imikolov/rnnlm/char.pdf


But since I learnt about them today, I would need a more vanilla introduction to this topic of validation.
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.