Artificial intelligence is ingrained in our lives in various forms. It analyzes a vast amount of data and provides insights for HR managers, marketing specialists, and store owners. For example, AI can hint if it’s time to optimize your Magento site’s speed or what your customers prefer.
As such, each of us has come across chatbots on online stores or while calling a bank support service. If you’ve ever said, “Ok, Google”, or asked Siri to play music, you already know what conversational AI is. It includes some elements to understand our speech and respond to us.
The system assesses each input to give the correct answer. And based on the result, this technology improves, using the information it receives. As we practice, our interaction with voice assistants becomes more rewarding and satisfying as if we could barely tell we are interacting with an AI at all.
But what is at the core of the bots’ work? How do they determine that the answer will match the request? That’s where a confidence score comes into play. This article will look at what a confidence score is and what role it plays in conversational AI.
Conversational AI Overview and How It Works
Let’s start with a quick overview of conversational AI. It is a system that brings together three different technologies:
Let’s assume that a person wants to get an answer to a question, order a product or service, or turn on music or video. This desire is called intent.
Then the user sends a request through some channels. They are, for instance:
- smart devices;
- voice assistants
- embedded devices
- mobile phones
- phone calls
- instant messengers.
Some channels involve voice communication, while other conversational platforms work with text. Therefore, they can convert the message format from voice into text. They are integrated with the corresponding APIs to work with interactive visual elements. Also, there are biometrics platforms that identify the interlocutor by voice.
The task of the conversational platform is to:
- understand the meaning of what has been said;
- capture the user intent;
- and efficiently process it.
The platform receives the data and generates a response. It can be a text, a voice message, a video, or a notification of a completed action (such as booking a table in a restaurant).
There are cases when the initial request doesn’t provide enough data to make a decision. So, the NLU (Natural Language Understanding) platform initiates a clarifying dialogue to get all the missing parameters and remove the uncertainty.
The system must understand the input to give an answer. To do this, it has built-in templates allowing to make a decision automatically.
Furthermore, various factors affect the quality of speech recognition. For example, noisy places or unclear wording influences speech-to-text translation or text-to-meaning accuracy.
If we describe the process in stages, then it will look like this:
- Receipt of a request to the system;
- Comparison of data with existing training phrases;
- Producing a response that matches the predefined statements.
Confidence Score Variations
A confidence score is a number from zero to one that every request receives. The developers themselves set the threshold between these parameters.
If you set it to 0, the system will accept every request, regardless of its accuracy. For example, the user asks for “cruelty-free and vegan shampoo”. However, there is no such request in the memory of the conversational bot.
Therefore, there are three options:
- The system can answer a similar, in its opinion, request;
- It can stop work and issue an error;
- It can ask a person to confirm the action.
If the threshold is at 0, it won’t produce an error and will continue the dialogue. It will answer the query that it considers closest. For example, “shampoos for oily hair”, which may not fully satisfy the client’s request.
For the robot to produce fewer mistakes and more accurate results, you need to increase the threshold.
The highest value of the threshold is 1. With this indicator, the result will be 100% consistent with the client’s desire. If the system encounters an unfamiliar input, it stops searching for information and issues a default message.
To avoid bottlenecks, you can use the human-in-the-loop approach. With this method, the system interacts with a person who creates a new hypothesis in the bot’s memory, making the model smarter.
So What Is the Confidence Score’s Role?
The role of the confidence score in conversational AI is that developers can determine which queries the system will handle. For example, whether it will accept all possible hypotheses or give an error.
Various situations require different confidence thresholds. A low confidence score ensures that the user receives a response and doesn’t get frustrated with a chatbot that responds with “I don’t know”. On the other hand, a higher threshold avoids irrelevant results and displays an option that will match the request best.
To Sum Up
Clients increasingly expect natural and human-like communication from chatbots. Different scenarios can affect customer satisfaction. And it, in turn, has a direct connection with the growth of your business.
Will the chatbot be able to satisfy the request? Will it be able to continue the conversation even if it doesn’t know the answer? It depends on how you set a confidence score in your system.
Primitive chatbots are a thing of the past, making way for intelligent, cognitive voice assistants. They will identify the request in noisy places, understand slang and accents, and even specify human emotions.
About the Author
Alex Husar, chief technology officer at Onilab. For over eight years, he’s been working on Magento migration and development projects as well as building progressive web apps (PWAs). Alex is an expert in full-stack development who shares his expertise and in-depth knowledge on modern technologies and Computer Software Engineering.