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Photo by 丁亦然 on Unsplash

Rasa-X Is A Unique Approach To Continuous Chatbot Improvement

And Why Conversation Driven Development Is The Future


When it comes to chatbot improvement, three elements are paramount:

  • Incremental
  • Contextual
  • Statistical Approach
  • Bulk
  • Metrics Focused
  • Hard To Steer

Why This Often Fails: Users Want To Follow The Desire Path

We talk about the happy path, and the repair path. The main aim of the repair path, is to bring the user back to the so-called happy path.

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Design versus Desire

Desire Path

The desire path usually represents the shortest or most easily navigated route between an point of departure and a destination. In parks and open areas, the width and severity of erosion are often indicators of the traffic level that a path receives.

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Left: Desire Path | Right: Designed Path


Landscapers sometimes accommodate desire paths by paving them, thereby integrating them into the official path network rather than blocking them. The image above is of an desire path being blocked and rehabilitated in an attempt to force users on the designed path.

Elements OF A Conversational Approach

From Designed Paths To Accommodation

Rasa took a completely alternative approach compared to other chatbot frameworks. By introducing something they call Conversation-Driven Development (CDD). This approach is encompassed in their Rasa-X environment.

Sharing Your Bot

The notion in Rasa-X to create a URL through which you can create a preview for users, reminds very much of feature IBM Watson.

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IBM Watson Assistant Preview Link

NLU Inbox: Turn NLU Training Into An Administrative Task

Utterances from users which are not part of your training data shows up in your NLU Inbox. These utterances can annotated and classified within the web tool.

Rasa X Overview: Installation & Functionality


Most probably you want to install Rasa X on your Windows 10 machine to play around with. I would suggestion you first install the Rasa chatbot software.

pip3 install rasa-x --extra-index-url
rasa x
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Rasa X Successfully Started

Start Talking To Your Bot

Before you can start talking with your bot, you will have to train your first model:

rasa train
rasa shell

Interactive Learning

You have the ability to talk to your bot and make use of interactive learning while in a conversation. This bridges the gap between practical experience and training data.

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Interactive Learning Chat Window

NLU Inbox

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List of Functionality Within Rasa X

Creating Intents & Compound Entities

Lastly I would like to spend some time on this one feature of Rasa X; managing and creating intents and entities. I see this as a sign that Rasa X will most probably evolve into a full-blown Graphic development tool.

  • Watson Assistant
  • Microsoft Power Virtual Agents
  • etc.
  • Studying how your users interact with the chatbot.
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Create a New Intent
I want to travel from Berlin to Stuttgart by train tomorrow.
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Example Utterance for Intent: travel_detail
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The word “Berlin” is selected and tagged as Entity type “from_city”
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Continuing of Entity Annotation
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Creating Entity Names or Types
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Adding a Second Intent Example


The challenge that an unstructured input environment like chabots pose is that you cannot anticipate every possible variation of user input.

Read More Here…

Written by

NLP/NLU, Chatbots, Voice, Conversational UI/UX, CX Designer, Developer, Ubiquitous User Interfaces.

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