I have a chatting app that works with predefined messages. The database has about 80 predefined conversations each with 5 possible responses. To clarify, here's an example:
Q: "How heavy is a polar bear?"
R1: "Very heavy?"
R2: "Heavy enough to break the ice."
R3: "I don't know. Silly question."
Let's say a user will choose R3: "I don't know. Silly question"
Then that response will have 5 possible responses, e.g.:
R1: "Why is that silly?"
R2: "You're silly!"
R3: "Ugh. I'm done talking to you now."
And each of those responses will have 5 possible responses; after which, the conversation will end and a new one will have to be started.
So to recap, I have 80 manually-written conversations, each with 5 possible responses, going 3 layers deep = 10,000 messages total.
My question: What would be the most accurate way of automatically generating more conversations such as these using machine learning?
I researched RNN: Karparthy's RNN post. Although RNN can make new content based on the old, the new content is quite random and nonsensical.