Rasa Debunks Five Myths Of Machine Learning & Chatbots

There Are Commonly Held Beliefs When It Comes To Implementing Machine Learning…

Introduction

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Emergence and Development of Conversational UI’s

Myth One: You Need Huge Datasets

Starting With The Basics

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Example Intent & Entities

Training Data

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Example of Intents, Entities & Compound Entities
## intent:bank_transfer- I want to transfer [R100](amount) from  my [savings](from_account) to my [credit card](to_account)- Can I transfer [R 2000](amount) from the [bond account](from_account) to the [credit card](to_account)- Can we move [R 50,000](amount) to my [credit card](to_account) from  my [savings account](from_account)- Let's move [R100](amount) from  [savings](from_account) to [investment account](to_account)- Let's transfer [R1000](amount) from  my [savings](from_account) to my [investment](to_account)- Take money from [savings](from_account) and place it in my [credit card](to_account), for the amount of [R 100](amount)- Transfer [R100](amount) from  my [savings](from_account) to my [credit card](to_account)- Move [R100](amount) from  my [savings](from_account) to my [credit card](to_account)- move money from [savings](from_account) and place it in my [credit card](to_account), for the amount of [R 100](amount)- Allocate [R100](amount) from  my [savings](from_account) to my [credit card](to_account)- I want to transfer [R100](amount) from  my [savings](from_account) to my [credit card](to_account)
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Intent & Entities Breakdown From User Utterance

Myth Two: Training Takes A Long Time

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My Laptop Specifications
# Configuration for Rasa NLU.
# https://rasa.com/docs/rasa/nlu/components/
language: en
pipeline:
- name: WhitespaceTokenizer
- name: RegexFeaturizer
- name: LexicalSyntacticFeaturizer
- name: CountVectorsFeaturizer
- name: CountVectorsFeaturizer
analyzer: "char_wb"
min_ngram: 1
max_ngram: 4
- name: DIETClassifier
epochs: 300 #100
- name: EntitySynonymMapper
- name: ResponseSelector
epochs: 300 #100
Training NLU model...
rasa.nlu.training_data.training_data - Training data stats:
rasa.nlu.training_data.training_data -
Number of intent examples: 405 (20 distinct intents)

Myth Three: You Need Highly Specialized People

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List of Functionality Within Rasa-X
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Managing Your Trained Models

Myth Four: Cloud Based Is A Must

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Migrate Your Cloud Chatbot To A Rasa Installation Anywhere

Myth Five: ROI Is Hard To Calculate

Conclusion

Written by

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

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