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I have a dataset of sentences of language X and Y (2 columns, for example, "abc def lang" ==> "xyz pqrt mno uages"). I want to have a output as a table that translates word by word (abc => xyz, def => pqrt mno, lang => 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

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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):

enter image description here

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