Photo by Ryan Wang on Unsplash

Eight Things Differentiating Rasa From Other Chatbot Platforms

And Why These Eight Elements Might Be What Your Conversational UI Needs.

Introduction

1: Ease Of Access

Software

One of my first Rasa Prototypes: RASA Interactive Learning and Conversation Visualization.
rasa init
rasa train
rasa shell

Hiding Complexity

Documentation

An Early Afrikaans Language Demo Using Tensorflow Pipeline

People

2: Conversation-Driven Development

Focusing On The Overlap

The Overlap Needs To Grow.

Mobile Apps Compared To Chatbots

Mobile Apps Compared To Chatbots
Traditional Chatbot Improvement versus CDD

3: Evolving Entities

Contextual & Compound

Compound & Contextual Entities in Rasa-X

Rasa’s Entity Roles

## intent:travel_details- I want to travel by [train](travel_mode) from [Berlin](from_city) to [Stuttgart](to_city) on [Friday](date_time)
## intent:travel_details- I want to travel by [train](travel_mode) from [Berlin]{"entity": "city", "role": "depart"} to [Stuttgart]{"entity": "city", "role": "arrive"} on [Friday](date_time)
I want to travel by train from Berlin to Stuttgart on next week Wednesday.
{
"intent": {
"name": "travel_details",
"confidence": 0.9981381893157959
},
"entities": [
{
"entity": "travel_mode",
"start": 20,
"end": 25,
"value": "train",
"extractor": "DIETClassifier"
},
{
"entity": "city",
"start": 31,
"end": 37,
"role": "depart",
"value": "Berlin",
"extractor": "DIETClassifier"
},
{
"entity": "city",
"start": 41,
"end": 49,
"role": "arrive",
"value": "Stuttgart",
"extractor": "DIETClassifier"
}
],
"intent_ranking": [
{
"name": "travel_details",
"confidence": 0.9981381893157959
},

Rasa’s Entity Groups

## intent:teams- The first team will be [John]{"entity": "teamMember", "group": "1"}, [Mary]{"entity": "teamMember", "group": "1"} and [Geoff]{"entity": "teamMember", "group": "1"} and the second groupto travel will be [Martha]{"entity": "teamMember", "group": "2"}, [Adam]{"entity": "teamMember", "group": "2"} and [Frank]{"entity": "teamMember", "group": "2"}.
The first team will be John, Mary and Geoff and the second group to travel will be Martha, Adam and Frank.
{
"intent": {
"name": "teams",
"confidence": 0.9999754428863525
},
"entities": [
{
"entity": "teamMember",
"start": 23,
"end": 33,
"group": "1",
"value": "John, Mary",

"extractor": "DIETClassifier"
},
{
"entity": "teamMember",
"start": 38,
"end": 43,
"group": "1",
"value": "Geoff",

"extractor": "DIETClassifier"
},
{
"entity": "teamMember",
"start": 83,
"end": 95,
"group": "2",
"value": "Martha, Adam",

"extractor": "DIETClassifier"
},
{
"entity": "teamMember",
"start": 100,
"end": 105,
"group": "2",
"value": "Frank",

"extractor": "DIETClassifier"
}

4: Deprecation Of The State Machine

5: Language Agnostic

language: "xx"  # your two-letter language codepipeline:
- name: WhitespaceTokenizer
- name: RegexFeaturizer
- name: LexicalSyntacticFeaturizer
- name: CountVectorsFeaturizer
- name: CountVectorsFeaturizer
analyzer: "char_wb"
min_ngram: 1
max_ngram: 4
- name: DIETClassifier
epochs: 100
- name: EntitySynonymMapper
- name: ResponseSelector
epochs: 100

6: Ease Of Configurations & Pipeline Changes

Anaconda Prompt running Rasa

7: No Specialized Or Dedicated People Required

Rasa-X Console

8: Full Control Of Your Data

Rasa-X: Managing your models. When “rasa train” is run, a new model is created. Activation of previous models is shown here.

Conclusion

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