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Since this sort of conversation is highly ambiguous and a difficult model to simulate lets first take the case of professional interactions which are more structured and so easy to simulate.
The number of interactions in a professional conversation can be around 10–20.
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.
Reward, could be the next steps the user takes, which could be clicking on a button, reacting negatively to bot’s prediction etc.
A good reward calculation results in the better learning model Bots need a personality so that they are human-like and have individuality.
Reinforcement learning techniques can be used here to predict the next possible intent, which could be of interest to the user.
In this approach, intents are grouped into clusters which have some common slots.
Generally, one inclines to club many intents to simplify the bot building process.
But, it would only lead to instability as the bot grows.
More intents mean the probability to hit the right intent is less. A good set of general and specific test cases are required to gauge the stability of a bot. Bots, which are considered to be stupid by an average human being will soon stop to exist.
Generic test cases are those common to any bot, it is a good practice to build and use generic test cases. The bot should not repeat itself; it should not ask obvious questions; in some cases, the bot should remember some information even across different sessions. Thus, it is important to match smartness of bots to that of an average human.