[2sk21]

Even the best attempts at task-oriented dialog currently using purely ML techniques are pathetically bad. Having worked in the field for a while, one thing that I have observed is that chatbots simply discard the bulk of the useful information that user provide them. They then fall back to asking users direct questions. Apart from this, they can only handle limited information retrieval tasks for which training data exists.

The next challenge in my opinion is to create a task-oriented chatbot that can help users to actually solve real problems which may not be directly related to previously seen problems. Related to this, consider the problem of creating a chatbot to automate support for an entirely new product.

I have played around a little in this space and feel certain that hybrid approaches will be necessary. For example, I created a car diagnosis/remediation chatbot driven by a Bayes net model of a car's subsystems. This actually showed signs of working - sadly got distracted with other projects.

[Animats]

I know; I've been playing around with "Rasa", a chatbot system based on Tensorflow. All the ML part does is match up canned answers with incoming questions. Someone has to provide all the answers and a few questions for each answer, then look at errors from user input and manually classify them for retraining. The rest of the system is just a template system for implementing phone trees.

Interestingly, MIT's START questioning-answering system is pretty good.[1] That project started in 1993, before machine learning, and it's more "traditional AI". Try it and comment.

[1] http://start.csail.mit.edu/index.php