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One which is applicable often is intent clustering.
In this approach, intents are grouped into clusters which have some common slots.
This looks similar to recommendation engine which works behind the scenes in amazon website — When you buy a book, your footprints are captured, translated into a vector and the recommendations are derived by looking at parallel vectors.
In a similar fashion, as the user is interacting with the bot, it has to identify conversation vector, look for parallel vectors and accordingly predict the next possible intent or intents and drive the conversation.
As more and more people are getting familiarised with chatbots, the ask for quality bots is only increasing. Here is an attempt to quantify the human-like behaviour of a bot.
That’s true, yet, there is a need to quantify the human-like behaviour of the bot.
Reinforcement learning techniques can be used here to predict the next possible intent, which could be of interest to the user.
That should be a fair enough way to handle ambiguity.Each bot should have its own identity and should avoid falling into a generic bucket.These days most of the bots are labelled as some kind of assistants. Bots can be specialists in a particular domain, analysts, observers and more. If bot developers ignore giving a personality to the bot, very soon they will be out of the race. Let’s take the case of children, where they know the language but don’t have knowledge.How many interactions does a typical conversation between two humans have?In the case of two friends chatting, the conversations could be endless (meaning interactions can even go into few thousands).