A Deep Reinforcement Learning Chatbot
arxiv.org
arxiv.org
But it was very interesting to see the 'next response' candidates for the two sample chats in Table 1 (p3 of the PDF). In particular : it was alarming to see how much their Deep Learning response selection mechanism had to chose not so much the best response out of a selection of decent responses, but more the most acceptable response out of a selection of mostly horrible ones.
I've used WIT.ai and API.ai (now Dialogflow), and they both require you to give a bunch of example sentences (e.g. "Yes", "Okay", "Sure"), assign them an intent ("YES"), and use that intent in your custom code (if intent is YES then...). I found this to be tedious and limiting.
The problems usually are not in training the thing, the problem comes with filtering the data.
There is usually a lot of meta- and memes in the chatlogs, and lots of answers come as a web link. Which is not always helpfull. Imagine a user is asked to pastebin a log. The neural net must expand this pastebins into the chat. Its very easy for a neural net to get lost in these logs or deduce the wrong thing from them.
Hope you have more success then i had with this.