question on wide and deep about unif

[geyingli]

I think the core value of wide and deep structure is the thought to structurally unify discrete and continuous features. So I wasn't intented to follow all the details from the original work. Another reason is that there are outstanding ideas proposed after wide and deep model came out, like attention machanism and BERT, which could further enhance the performance of the model.

The answers are:

1. The input of wide side could be any discrete features. It can be a text string, an interger or even float if you want.
2. Attention machanism was proved to be a successful design. Using it properly improves the performance of NLP tasks (in most of times).

Hope this reply meet your needs :)