I am a bit confused as to how to use different sets of source and target vocabularies in deep learning for NLP tasks.
- What are the implications of using separate source and target vocabularies (for example, in machine translation, or in general)?
- Should the indices for corresponding tokens match in the word embeddings?
- How should their sizes be relative to each other?
- Do you have to modify the architecture in any way to accommodate this, or could you simply change the path to vocabulary?