I am curious to implement and try a model similar to the one given here:
I am planning to use POS and Named Entity labels as additional features for the translation of two languages. However, I have a few gaps in my thought process as I am quite new to this field. I hope some researchers could help me think in the right manner here.
The encoder here takes all the additional features using a ParallelInputter, meaning that I need to add the features to my source language sentences. How do I make use of similar additional features for my target language sentences as well? Like, how can I make the model learn relationships between various source and target POS tag sequences? How can I help the decoder narrow down its search by making use of the possible features in this case?
When I do an inference using such trained model, is it necessary to input the sentence, POS tag sequence and the NE sequence along with the source sentence?