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.:

  1. (Fabic, Alis, Brooty, Morange etc) are potentially good fruit name
  2. (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:

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


1 Answer 1


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.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.