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I have a passion for good UX. I also have great disdain for poor user experiences. Over the past few years, I’ve had the pleasure of working with many clients to design and build virtual agents that use speech technology. I’ve also enjoyed the opportunity to provide assessments for existing cognitive speech solutions. There is one fundamental concept which I find continually reinforced by these experiences.

The biggest takeaway I have learned over the years is that a good user experience doesn’t ‘just happen’; it happens as the result of intentional, thoughtful design…


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When you are building a Watson Assistant chatbot that must handle a variety of knowledge domains, you may begin looking into a concept known as “multiple skills orchestration”. Natural language classifiers often perform best when the classifier is focused on a single domain. Some use cases will perform just fine when mixing domains in a single skill (such as a banking use case that also handles light chit-chat). However, if your chatbot needs to demonstrate a broad range of knowledge and is struggling with accuracy and confidence, the solution may benefit from isolating each domain as a separate skill. …


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Photo by Christian Wiediger on Unsplash

If your enterprise has a virtual agent (chatbot) or is thinking about building one, you will want to understand how the solution can deliver business value. “Containment” is one of the top metrics a conversational solution will be measured against. This term refers to how often the bot is able to resolve a user’s request without escalating to a human agent.

Every successful transaction within a bot can be calculated as cost savings. Business value is often realized once the solution can contain enough transactions to cover the cost of the implementation and ongoing maintenance.

Many solutions are capable of…


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In Part One of this series, we covered what it takes to get your team up and running to start building a natural language classifier. Now let’s continue with some specific guidelines for working with your data.

Best Practice #4: Define Each Intent

Intents are purposes or goals that are expressed in a user’s input. It is important to group examples according to what the user’s goal is — from the user’s perspective.

The most common underlying problems in every poor performing model I’ve encountered are intents that are either too broad or too specific (or a combination of the two). It’s not always easy to…


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Photo by Dylan Lu on Unsplash

Natural language classification is a component in many AI-powered solutions such as chatbots, virtual agents, and agent assistants. IBM’s Watson Assistant and Watson Natural Language Classifier (NLC) products leverage powerful machine learning techniques to extract meaning from a user request. However, the underlying classifier must be trained in your business domain and use case. This training activity tends to be an aspect of AI projects that is not well understood — yet it is probably the most crucial because it is the foundation on which the entire solution is predicated.

Natural language classifiers are built using a supervised machine-learning approach


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Photo by Drew Graham on Unsplash

As chatbots continue to be in high demand for companies who want to automate or provide 24x7 customer service, many still struggle with understanding what is necessary to get a chat solution off the ground. Simply put, AI needs data. However, not just any data will do.

When you build a chatbot, you must train it to understand the questions or requests that are most likely to be posed to the solution. This is the most common misunderstanding I encounter. Many organizations have plenty of “data” in the form of answers that they wish to provide within a chatbot. …

Cari Jacobs

Cognitive Engineer at IBM Watson. Interests include natural language processing, user experience design, and martial arts.

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