That's a domain specific chatbot. There are a bit too many factors in how your current data for answers is stored to be specific. I would look at various approaches to domain specific chatbots and then see which one is most applicable for you. This paper might be a starting point: https://arxiv.org/ftp/arxiv/papers/2001/2001.00100.pdf

One piece of advice I would personally give is read a lot before starting such a project. Better to have a clear plan than try to establish as you go.

Thank you that's awesome. Super surprised to see its a tuned CNN, I've been going FCNN. Very interesting, you've made my day.

What's the smallest number of parameters you've seen for MNIST? I've been golfing with myself at it and managed to get 99% validation accuracy at 2922 parameters. I'm wondering if anyone has done lower?

I've had success with Squeezenet for finding the origin of white circles in extremely noisy images, so maybe you could use that. Just change the last convolution in the classifier to match the desired dimensions of your output.

I was able to CPU train a solution that way. It's actually my go to for tasks like that because it seems to just do better than some larger newer networks at that sort of thing.