Should I have two set of word vectors(word2vec), one for questionSet and one for answerSet before neural network training?

Question

I am confuse with when building a machine learning chatbot for a close domain topic about car.

I have lot of text format information about different car models and do a Word2Vec process with these data and saved a Word2Vec.model.

Then question sets and answer set will be converted to vectors by looking up in the word2vec.model. And finally, put them to a seq2seq model for network training.

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My questions:

1. Should I build two word2vec.model instead of one?, e.g. word2vec_question.model and word2vec_answer.model? and feed to question-set and converts to vector based on word2vec_question.model while answer set according to word2vec_answer.model ?

2. Why there are chatbot examples do not use Word embeddings but just tokenised those question-set and answer-sets and go straight to seq2seq training? Is this because the conversation sets are huge enough to train the S2S network without vectorise? Should we say that if data is hugh enough, just tokenise is enough and no need to do word2vec modelling?

3. Back to my car expert system, please give me some advise what the right way to prepare the data and feed to the Q&A examples. My ultimate wish is that, every week, I feed the word2vec model(s?) with information from car magazines (not in conversation format but just passages about new cars) then, the chatbot can answer questions also about that new model.

Thanks in advance.