I'm looking to make an NLP model that can achieve a dual purpose. One purpose is that it can hold interesting conversations (conversational AI), and another being that it can do intent classification and even accomplish the classified task.
To accomplish this, would I need to use multimodal machine learning, where you combine the signal from two models into one? Or can it be done with a single model?
In my internet searches, I found BERT, developed by Google engineers (although apparently not a Google product), which is an NLP model trained in an unsupervised fashion on 3.3 billion words or more and seems very capable.
How can I leverage BERT to make my own conversational AI that can also carry out tasks? Is it as simple as copying the weights from BERT to your own model?
Any guidance is appreciated.