Picking the right NLP model to tag words from a dataset

Question

As the title suggests, I am posting here in the hope someone could direct me towards NLP models for tagging words.

To be more concrete, here is what I wish to do. I would like to build a flashcard application using an NLP model that would tag/categorize words. So let us imagine I have a CSV file with items made of one question (in English) and one answer (in French):

+----------------------------
| plane       | avion       |
+-------------+-------------+
| chopsticks  | baguettes   |
+-------------+-------------+
| airport     | aéroport    |
+-------------+-------------+

The idea is that the learners would pick a contextual deck (in this example, a deck related to travelling with planes). That deck would be generated by a tag "airport" made by the machine learning algorithm.

And thus, is there any good models I should look to?

Edit:

After much research, I came across NLU which meets many of the requirements I have described above. If you are interested, please have a look at those links: What is NLP technique to generalize manually created rules in text? and NLP algorithms for categorizing a list of words with specific topics, as well as this repo: Probase-Concept

Answer 1

For generating lists of words related to the same topic, my first thought would be to take a large corpus of (monolingual) text, apply topic modelling and then collect random top words by topic.

Answer 2

You could look into sequence-to-sequence models. These model can be used to "translate" text. There is a nice example in Keras, where you can straight away build a translation model for English -> French.

This example demonstrates how to implement a basic character-level recurrent sequence-to-sequence model. We apply it to translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-level models are more common in this domain.

Also note this blogpost "A ten-minute introduction to sequence-to-sequence learning in Keras".