Implementing a model for a language to another I have a dataset of sentences of language X and Y (2 columns, for example, &quot;abc def lang&quot; ==&gt; &quot;xyz pqrt mno uages&quot;). I want to have a output as a table that translates word by word (abc =&gt; xyz, def =&gt; pqrt mno, lang =&gt; uages).
I think we should use deep learning but idk how to implement and what to use. Can you give me some ideas?
Thanks all
In case you want some state-of-the-art technique, you can implement a neural machine translation model with attention, as fully described in this course with Google members libraries like Trax.
In case of short texts, you can still use sequence-to-sequence models without attention, but the attention mechanism prevents you from suffering the vanishing gradient problem on longer sentences.
As a very brief schema of the type of model, find the image below (from the same given source):