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NLP Chatbot Resiliency: A Chat With Botpress

In the race to design great conversational experiences, adaptable NLU models will play a key role in the creation of truly intelligent chatbots. In this article, learn how Botpress stemmed from frustrations with poorly designed bots that led to a launch of an open-source managed NLU platform. Also, see why the future of chatbot design will shift from an intents-based approach toward knowledge-based models that offer greater adaptability and resiliency.

Developer Accessibility Key to NLP Chatbot Advancement

Chatbots have come a long way over the years, evolving from simple command-response models to the more nuanced NLP conversational models of today. 

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How To Measure the Success of a Conversational AI Chatbot

Illustration by Quovantis

Last year, when one of our healthcare partners (we refer to our clients as partners) were looking to build a conversational AI chatbot, I was apprehensive about guiding them. I had only worked on the level 2 (out of the 5 levels of conversational AI) type of bots. But this time our partner wanted to build a contextual/consultative AI-powered chatbot assistant.

I was concerned about how the bot would understand end-users’ problems. What features can we build to make it more humanistic? Would it be successful in replacing human care and compassion? Would it replicate the same emotions of empathy, compassion, and care?

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Modifying Your Virtual Assistant to Use Custom Entities – Here’s How You Do It in Teneo

Virtual assistants and chatbots are great tools for improving customer service in any company. However, to be able to become a great customer service agent there is some work to do to make it fit your business needs.

Businesses today have their own way of naming things and the same way you would need to train any co-worker on the business vocabulary, you need to train the virtual assistant. That is when customization starts.

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Chatbot Scripting: Storing Input Parameters From Client Applications in Teneo

Besides the natural language inputs of the user, client applications can also include input parameters in their requests to Teneo. The values of these input parameters can then be stored in for example global variables, so that they can be used by flows, integrations, etc. 

More details on how client applications can interact with Teneo can be found on the Teneo Engine client API page in Deploy your bot.

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Teach Your Conversational AI Application to Store Information in Teneo

To be able to create a humanlike conversation there is a need to teach your virtual assistant to remember inputs that the user says, for example, the user’s name:

User: I want a small cappuccino.
Bot: Ok, what name shall I note for the order?
User: Amber.
Bot: Thanks for your order, Amber! A small cappuccino will be ready for pickup in 5 minutes.

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Making Sure Your Chatbot Can Get the Conversation Back on Track

A good dialog system is able to get the conversation back on track if the user makes an ambiguous request. In this walkthrough, we will look at the flow we built in create a custom order group and expand it to help users find the correct answer to their question. We will do this using flow links, which allow us to send the user to a flow meant to answer the question. In the Partial understanding: coffee flow we will first present users with likely options of what the question might have been, and allow users to give a clarifying response. You can read up on flow links on this page.

Before our changes, we would have a conversation like this:

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Why Most Chatbots are Annoying and How to Make Sure Yours Isn’t

As conversational language interfaces begin to dominate customer service, so does the backlash against chatbots grow. Forrester predicted last year that 2019 would be the year of the backlash against inefficient chatbots, and it looks like they were right. For example, a survey commissioned by an open software service company Acquia, that analyzed responses from more than 5,000 consumers and 500 marketers in North America, Europe and Australia, found that 45 percent of consumers find chatbots “annoying.”

At the same time, the importance of conversational AI for business today cannot be overestimated. When done right, conversational AI has the ability to significantly increase your competitive advantage and fundamentally change the nature of business-customer interaction.

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How Your Chatbot Can Learn to Understand Synonyms in Teneo

In language, there are many ways of saying the same thing. That creates a need to optimize the language conditions for your bot so that it can give the correct answer even when other words are used. Here is how you do it in Teneo Studio.

We have earlier seen examples of how to semi-automatically create language conditions based on positive example inputs. For example, we created a syntax trigger that can handle conversations like the following:

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Teach Your Conversational AI Chatbot to Pick up Entities in Teneo Studio

Teaching a conversational AI chatbot to pick up entities.

In order for your bot to understand what the user said, some words of the user's utterance are more important than others. Typical examples for such important words include so-called named entities like cities or product names. Here is how you pick up an entity from user input in Teneo Studio.

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Sometimes it's not enough to recognize which flow to trigger. Your bot may also need to extract some piece of information from the input to respond appropriately. Let's assume that the user wants to know where our Longberry Baristas stores are located. This is how such a conversation could go about: