5 Handy Tricks for Improving Your Chatbot Understanding with NativeChat

5 Handy Tricks for Improving Your Chatbot Understanding with NativeChat

Posted on May 22, 2020 0 Comments
5 Handy Tricks for Improving Your Chatbot Understanding with NativeChat_1200x620

These five quick tips will help you build chatbots with a greater understanding of the user's input and avoid common natural language processing pitfalls when using NativeChat.

One of the key things that we can teach our chatbots is to analyse the user's input and redirect the user to the correct conversation. This is done by training our chatbots with a set of Triggering Expressions.

In this article, we will look at five NativeChat tips and tricks on how to build chatbots with a great understanding of user input.

If you are not familiar with Natural Language Processing (NLP) and how it works, then you should start with this article: What is Natural Language Processing?

Triggering Expressions

Triggering Expressions are sample expressions/sentences that we train our chatbots with to recognize specific Conversations.

The idea is that if we have a Q&A type conversation:

  • name: best-pizza

  • triggering expression:

    • "What is the best pizza?"
    • "Number one pizza"
  • answer: "Hawaiian Pizza is the best. 🍍+ 🐷 = πŸ•πŸ˜"

So that when a user says something like: "What is the number one pizza?" The chatbot should match it to the best-pizza and respond with: "Hawaiian Pizza is the best. 🍍+ 🐷 = πŸ•πŸ˜"

Tip #1—Test with Understanding

As we train our chatbot and provide it with new triggering expressions (which should trigger a specific response), it is good to test the new expressions as we go, and see what your chatbot would respond with.

NativeChat has a feature called Understanding, which allows you to test chatbot's understanding of various expressions. You can find it in the test window by switching to the Understanding tab.

Show Understanding

To use it, just type any expression, press Enter, and NativeChat will respond with:

  • the best-matched conversation
  • the confidence score

Note, confidence above 65% means that the chatbot would accept that conversation as a valid match.

Test with Understanding

You can also use Understanding to see what Entities the chatbot recognises.

Understanding - Entity

It is good practice to regularly check the changes that we make to our chatbot's training, and make sure that we get the results that we expect.

Tip #2—Run Problems Check

Sometimes, when we add a new conversation with a set of triggering expressions to our existing chatbots, it is possible that the new triggering expressions could interfere with other conversations. As a result of which, it is possible that adding a new conversation to our chatbot could make our chatbot not recognise conversations that used to work just fine.

It would be quite hard to manually test every conversation we have in the chatbot. However, NativeChat has a feature called Problems Check that allows you to test all the triggering expressions, and validate them against the conversations that were used to create the NLP Training Model.

You can find the Problems Check page by either:

  • going to the Dashboard page and then navigating to the Problems Check page
  • going to the Training tab and then choosing the Problems Check sub-tab

Then start the test by pressing the Run a quality check button.

For each triggering expression, you will see the expected conversation, the matched conversation, and the confidence level of that match. Like this:

Problems Check

Problems Check is a very useful tool to quickly check if any conversation doesn't get triggered as expected. This way you can identify potential issues, and make sure your user experience stays top-notch.

The Very Similar Triggering Expressions Problem

Sometimes, you might have multiple very similar triggering expressions, which are so close to each other that they fail to match even for exact matches.

For example, you might have four conversations with triggering questions like: "What is the location of officeX?"

Example: conflicting expressions

When we run Problems Check against that set of triggering expressions, NLP will fail to match any of these four conversations.

Example: conflicting expressions test

This is because, in each case, NLP will match (What), (is), (the), (location), (of), and (?) tokens (words/punctuation marks). While the match to (officeX), will either be exact or too close for the NLP to decide which of the triggering conversations is strong enough to be returned as the correct result.

Let's face it, "What is the location of officeA?" looks very similar to "What is the location of officeB?".

The thing is that the only part that differentiates each of these triggering expressions is the name of the office. There are various ways in which we could use that to solve the problem at hand.

In the next three tips, we will see three different ways of solving this problem.

Tip #3—Highlighting Important Words

We could solve the above problem by highlighting more important words.

Highlighting is a technique in which we provide additional triggering expressions to the conflicting conversations. The additional expressions should contain the words that we want to highlight with the NLP as more important.

"If the word is so nice, you should say it twice."

In our example, we need to add more importance to the officeX name. This can be done by adding a second triggering expression to each conversation, which would repeat the name of the office, like: "Where is officeA located?"

This way, NLP will understand that officeA is very important, and it will prioritize matches to officeA above other matches.

This is how you can update all failing conversations, and rerun Problems Check.

Highlighting Important Words

After we rerun Problems Check, all the triggering conversations should get a high-confidence match.

Highlighting Important Words Test

It is good practice to use the highlighting technique to tell your chatbots which words should be treated with more importance.

This is useful both in the case where you have a lot of very similar triggering expressions, but also in general so that your chatbot will always understand what words it should pay special attention to.

Conclusion: Provide your chatbot with multiple expressions, to help the chatbot identify the keywords.

Tip #4—Remove Stop Words

We can also improve NLP matching by looking at the Stop Words. These are words like: is, are, my, to, the, a, an, of. Words like that usually don't bring much value when it comes to NLP matching process. On the contrary, Stop Words tend to add more unnecessary variables.

Another way to solve the original problem could be achieved by removing the Stop Words (is), (the) and (of) from: "What is the location of officeX?". As a result, each expression should look like: "What location officeX?". Like this:

Remove Stop Words Fix

When we rerun Problems Check—note that we are not including the second expression used above for highlighting—we should once more get all successful matches like this:

Remove Stop Words Test

Also, when we run the Understanding Test for: "What is the location of the officeA?", we still get a successful match to locationA, like this:

Remove Stop Words Understanding

It is good practice to remove Stop Words from triggering expressions. This way we only keep the words that are useful for NLP matching, and as a result, we should expect more consistent results.

Conclusion: Make the training expressions concise, to help the chatbot identify the keywords.

Tip #5—Convert Similar Q&As to Guided Tasks

We can also use a Guided Task to teach our chatbot how to respond to very similar questions, where the key difference is just the name of the entity (like the office name).

The idea is to create a Guided Task, in which the chatbot is aware of all office entities—together with info like their address or phone number—that provides the address for the requested office.


The chatbot should be able to handle the following scenarios:

Scenario A: Where is your (office-name) office?

When the user provides an office name, the chatbot should identify the office, and then return the address.

🦸🏻‍♀️ Where is your Boston office?
πŸ€– The address is: 14 Oak Park Drive, Bedford, MA 01730

Scenario B: Where is your office?

When the user asks for the location of an office but doesn't provide the name of that office. Then the chatbot should prompt the user to specify which office and provide available options. Finally, when the user selects an office, the chatbot should respond with the answer.

πŸ‘¨πŸ½‍πŸ’Ό Where is your office?
πŸ€– Which office are you interested in?
πŸ€– (Boston) (London) (Sofia)
πŸ‘¨πŸ½‍πŸ’Ό Boston
πŸ€– The address is: 14 Oak Park Drive, Bedford, MA 01730

A High-Level Explanation of How to Create a Guided Task

Please, note that we will not get into too many details on how to create a guided task here

Step 1

Create an entity for the office. This is where we instruct what offices the chatbot should be able to recognize, together with attributes like address and phone number for each office.

Office Entity

Step 2

Create a new conversation in the Cognitive Flow.

This conversation has two jobs:

  • ask the user: "Which office are you interested in?"
  • and when we have the office entity, then provide them with the address

Here is how the code for a conversation like this looks:

"office-location": {
  "type": "goal",
  "steps": [
      "type": "question",
      "entity": "office",
      "entity-type": "Office",
      "messages": [
        "Which office are you interested in?"
      "display": {
        "type": "quick-reply"
      "type": "message",
      "messages": [
        "The address is: {{office.address}}."

Step 3

Add conversation trigger, to tell the chatbot when to trigger this conversation.

We can use expressions like:

  • Where is office?
  • What is address for?

Office Conversation Trigger

Step 4

And now we can test our new conversation, which should be able to handle both scenarios:

  • Where is the Boston office?
  • Where is your office?

Like this:

Guided Task Test 


Although building Guided Tasks requires more work up front, it makes it a lot easier to add a new office without having to change the flow of the conversation, and also it gives us a lot more control over the flow of the conversation.

How to Create a Guided Task from Start to Finish

You can watch the following video to learn how to create this Guided Task in less than five minutes.


When building chatbots and training them to understand user input better, you should follow the following rules:


  • Test often with Understanding
  • Use Problems Check to test all triggering expressions

Use NLP Techniques to Help Your Chatbot Identify the Important Words

  • Highlight Key Words—Provide multiple training phrases
  • Remove Stop Words—Make expressions concise

Use Guided Tasks

  • Convert similar Q&As to Guided Tasks

Try NativeChat

If you would like to build a chatbot, NativeChat is an innovative AI-driven platform for creating and deploying secure cognitive chatbots that automatically learn and adapt to generate a natural conversation flow for customers on the channel of their choice—web, social or mobile.

You can learn to build a chatbot in a few hours. Just follow this step-by-step tutorial and soon you will have a chatbot that can both answer straight questions and answers, and also handle more complex conversations.

Start Your Chatbot Tutorial

Sebastian Witalec

Sebastian Witalec

Sebastian Witalec is a Senior Developer Advocate for Progress who specializes in Angular and NativeScript. He loves working on both serious and fun projects and one day he will use his robot army to conquer the world.


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