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How to use Chatbase to improve your bot

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I recently spent some time watching the Chatbase team’s video on improving chatbots using Chatbase.

I will summarize the material in this post.

What is UMM?

UMM is an acronym which stands for:

U – Unsupported requests

M – Misunderstood requests

M – Missed requests

Let us take a look at some examples to show what each of these types of requests are.

Intent Mapping

For the sake of explanation, let us suppose you can assign a color to each of your intents.

So you have red, blue and green intents, and the black color represents the Fallback intent.

Unsupported requests

Suppose, we get a new user request and it is an orange. We don’t (yet) have any way to handle it. So it gets mapped to the Fallback Intent.

This is an example of an unsupported request.

Suppose after seeing this, we add an orange intent into our list of intents.

Misunderstood requests

We now get user phrase 5 which should be mapped to the newly added orange intent. Unfortunately, it gets mapped to the Red Intent instead. This is an example of a misunderstood request.

Missed Request

We get another request User Phrase 6. This should have been mapped to the Blue Intent, but instead it gets mapped to the Fallback intent. In this case, we have a missed request. It should have been mapped to the blue box.

How Chatbase can help

So we have now seen the different categories of chatbot errors.

How can Chatbase help us fix these errors?

Not handled

Chatbase has a concept called “not-handled”. If a user request gets mapped to the Fallback Intent, you set this flag and notify Chatbase using its API.

Unsupported and Missed Requests

You can take a look at the not handled requests and you will be able to identify your missed requests and your unsupported requests.

Misunderstood Requests

It is a little more challenging to identify misunderstood requests. In this case, the phrase which should have been mapped to Intent A gets mapped to Intent B instead. This means you will not be setting the not-handled flag when you send details about this request to Chatbase.

The Chatbase team suggests looking for intents with high exit rates as a sign that you have a lot of misunderstood requests. Personally, while I think this might be a good starting point to identify misunderstood requests, you need to dig a little deeper if you wish to identify most of the misunderstood requests.

Summary

Interestingly, the color analogy can be used in quite a few places as you try to reason about your chatbot. I will try and create some learning material on this topic in the future.

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6 Comments

  1. Hai Aravind,
    Could you please explain how to integrate dialogflow chatbot with chatbase ,i’m struggling in the fulfillment code , can you please explain in detailed steps

    Many thanks,
    Finocia

    1. Generally speaking, I found Chatbase a little limited. So I haven’t looked into it recently.

      I recommend you ask on StackOverflow.

  2. Hey Aravind,

    I wanted to thank you for putting out such great training and posts!

    Regarding chatbase, and it’s the ability to store the entire conversation, have you leveraged these conversations as a way to store and then utilize this history when a user returns to your bot?

    For example, if you have a travel bot, and a user returns a month later, it would be slick if you could greet him with something like, ‘do you want to continue your booking for Rome?’.

    I’ve searched your posts to see if you’ve addressed this previously but didn’t find anything, sorry if you are repeating yourself.

    1. You don’t need Chatbase to do this, but you do need your users to be authenticated and you need to maintain logs of conversation history in a structured way in your database. Then you can do a lookup when the user comes back and see which intent they triggered last time they were talking to your bot. Based on the last intent (or intent sequence) you can actually trigger an intent using the event feature. Would that work?