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Decoding chatbot design – A matter of intent & entity

Aishwarya Sinha Ray
min read
Decoding chatbot design – A matter of intent & entity

Design your chatbot with intent and entity, that's what every chatbot designer says. Here's an example to help you understand this better – If you’re into reading, what according to you matters more – author’s intent or reader’s interpretation? Some say that the thoughts of the author matter more. It’s their story, their perspective and something they design and want us to believe in. Others support the reader’s interpretation because the reader is the one on the receiving end. Even when two different people discuss the same book, they might have two different opinions. It’s completely subjective.

However, in the case of human-technology interaction where machines design a story for the customer or build a conversation, the customer interpretation is always going to be the winner. There’s nothing subjective there. That’s why they say, “The customer is always right!” Therefore, while developing and designing a chatbot it’s always a matter of customer intent and entity recognition.

Design your chatbot to design the best customer experience

Customer input and underlying intent

One of the first steps that a Chatbot ui designer focuses on while working on a conversation project is picking up recurrent customer questions. An easy way of going about it is through collecting data from customer queries through calls, live chat or other channels. However, at times the language is not concrete enough for humans to understand, let alone chatbots. Here are 2 versions/inputs of the same situation-

Input 1 – Suggest a good Italian restaurant nearbyInput 2 – My father likes Italian and I’d like to take him out for dinner

Here, Input 1 is clear and direct while Input 2 expects the chatbot to understand the underlying customer intent, which is to look for a nearby good Italian restaurant. The entity that the chatbot can possibly pick is ‘restaurant’, ‘dinner’, ‘take <noun pronoun=""> out’, and respond accordingly.</noun>

Another example could be something like this-

Input 1 – Where can I get my broken iPhone screen fixed?Input 2 – I have a broken iPhone screen

Obviously, Input 2 suggests that the customer is looking for an iPhone repair shop. The entity in this example would be ‘broken’, and something that is broken must be fixed. Therefore, you've got to design the structure to support the same. So, NLP is training and designing ui chatbots to understand intent and the context of the conversation and that will further help Conversation Designers to create the dialogue flow and relevant content.

Humans, feelings, and chatbots

Let’s look at intent, layout and entity identification from the customer’s perspective. What is it that customers, or people in general, do when they want to express their feelings, emotions, intentions, or requirements? Like we say in the examples above, customers either put their requirement directly out there or play around with a myriad of words. They can either ask questions, use exclamations, or keep browsing with the available options that the chatbot provides. They do what they feel is expressive enough and doesn’t call for extra effort, unless they are extremely frustrated. So, how do you go about the design for the chatbot? Of course, it is easy to understand what the customer is looking for when they directly state it but at times even human inputs can be vague and outrageously confusing.

So, when you’re running a business and want to build a bot you’re left with either of the 2 choices-

One: Constantly ask the customer, “Could you please repeat that?”
Two: Constantly train the chatbot to understand customer intent with the help of NLP

The first one is a red flag for your business. The second one, however, is a powerful tool to bet your money on, and why not? Machines aren’t unruly anymore. It was an old estimation that machines can work in a systematic order but not in an intelligent manner; perhaps, back in the 50s but not today. The advancements in technology and NLP are training chatbots to compete with humans in the most technically challenging and intellectual fields. This is the kind of intelligence and design that is inbuilt in chatbots that trains them to understand customer intent in a conversation.

Contextual awareness and emotional intelligence

We are building a conversational interface that is helping machines train themselves to interact with humans. Thanks to neural networks, which map responses over gradients to make the responses more analogically proportionate. That’s how bots are smart enough to collect customer queries, provide efficient customer solutions, figure out what answers they don’t have, and train themselves to learn and bridge the gap. This training is making chatbots contextually more aware and emotionally robust and intelligent. The overall design works well for business.

Moreover, humans are evolved and the rulers of this planet but let’s be honest – human conversations don’t always make sense. A typical example is from your daily chats with friends, family or someone at work, and you requesting them to repeat what they mean because their words don’t quite add up. The conversation may look something like this-

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed design eiusmod tempor incididunt ut labore et dolore magna aliqua. Reschedule my flight to Dallas. Duis aute irure dolor in reprehenderit in voluptate velit esse design cillum dolore eu fugiat nulla pariatur.

The customer intent is to reschedule the flight but it may also be surrounded by information that is not relevant for the chatbot. Therefore, chatbots understand the context, pick the intent, match it with an entity, and at the same time empathise with the customer.

For more on intent entity recognition and chatbot design technology, please visit Engati!

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