There has been a huge hype in chatbots or digital assistants in recent years and in the post-pandemic era digital assistants are likely to become a new normal. In this blog, I will try to explain what are the major components of chatbots and how these components can collectively be used for various inferencing tasks.

Understanding the Conversation

The first and most important part of a chatbot is to understand the conversation. To understand a query better we first need to identify, “What the user is asking?”, “What information the user has provided?”, “What is the context?”, and “What is the tone or sentiment of the user?”

The most important of these is “**what the user is asking?”, **or Intent (Intention of User).

What is an “Intent”?

An Intent is the bot user’s intention. It is used to understand what the user wants to do.

Suppose you are building a bot for the banking domain, the bot users are expected to check their account balance or find the nearest branch, these can be represented as intents like “check balance” or “find branch” respectively. These intentions along with the other entities, or traits extracted (which gives more context), are utilized by the inference engine to take appropriate action.

There are many ways to identify the Intent of a query,

Rule-Based Approach: Using Templates or Rules based on various Linguistic Features (like POS tags, lemmas) or regex patterns.

Machine learning Approach: Using machine learning techniques like SVM, NB, Logistic Regression, or any advanced neural network approaches, along with Embeddings to capture more context or meaning of the bot user.

Intent can be used to take different actions like Information Retrieval, Work Flow Execution, Chit Chat(Small talk).

#dialog-management #chatbots #conversational-ai #ai