What is currently the best way to add a custom dictionary to a neural machine translator that uses the transformer architecture?
## ❓ Questions & Help

It's common to add a custom dictionary to a machine translator to ensure that terminology from a specific domain is correctly translated. For example, the term server should be translated differently when the document is about data centers, vs when the document is about restaurants.

With a transformer model, this is not very obvious to do, since words are not aligned 1:1. I've seen a couple of papers on this topic, but I'm not sure which would be the best one to use. What are the best practices for this problem?

One paper I found that seem to describe what I'm looking for is [here](aclweb.org/anthology/W18-6318.pdf ) - I have a bunch of questions regarding the paper, which I'm happy to discuss here as well. I'm also wondering if there are other approaches.

This question is too general for this repo. It's not specific to anything this repository offers. Perhaps it's better to ask this on one of the Stack Exchange sites. <|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
