About This Playbook

This playbook represents a first step toward defining the governing principles and best practices which will enable developers to build great conversational applications. It is the result of several years of practical experience building and deploying dozens of the most advanced conversational applications achievable. Cutting-edge research and state-of-the-art algorithms are not surveyed here; there are many other resources available for that purpose. Instead, this playbook focuses on helping developers and data scientists build real production applications. The detailed instructions, practical advice, and real-world examples provided here should empower developers to improve the quality and variety of conversational experiences of the coming months and years.

The Introduction provides an overview of the state of the art in building conversational applications and discusses tradeoffs associated with different implementation approaches.

The Step-by-Step Guide illustrates how to build an end-to-end conversational application using MindMeld, the Conversational AI toolkit. You can apply it directly while building a conversational application, or just read it to learn the essentials of MindMeld.

The Blueprint Applications section explains how to use MindMeld to quickly build and test a fully working conversational application without writing code or collecting training data. You can bootstrap a Blueprint app into a more specialized and powerful conversational application, or just use the Blueprint app as a demo or research tool.

The User Guide covers each component of the MindMeld platform in depth and explains how to use MindMeld to build and configure each component for optimal performance. It functions as a technical supplement that details how to improve an application built following either the Step-by-Step Guide or the Blueprint Applications. The User Guide highlights many common techniques and best practices that contribute to successful conversational experiences today.

The Versions section contains a Recent Changes section which you should always consult before installing or upgrading MindMeld.

Readers should be familiar with Machine Learning and the Python programming language, including functional knowledge of the scikit-learn python package.

For those who wish to use the deep learning models included in MindMeld, familiarity with the TensorFlow library is recommended.

How to Use these Docs

The questions and answers below explain how to use this Conversational AI Playbook to support your individual needs as a developer and learner.

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Q: What is Conversational AI? Why does MindMeld exist and what does it do?

A: Read the Introduction. This explains what MindMeld does differently from other platforms and toolkits.

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Q: What is the methodology for building Conversational AI applications with MindMeld?

A: Read the Step-by-Step Guide. This and the User Guide together form the main body of knowledge for MindMeld developers.

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Q: How do I quickly get a pre-built MindMeld app running, either as a learning exercise or as a baseline for my own application?

A: Follow the instructions in Blueprint Applications.

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Q: How do I apply the Step-by-Step methodology to my own MindMeld app, either building from scratch or extending a Blueprint app?

A: Work through either the Step-by-Step Guide or the section of Blueprint Applications that’s appropriate for your use case. Where those docs refer you to the User Guide, apply the in-depth material in the User Guide to improve your application’s performance or increase its sophistication, as needed.

The User Guide and the Step-by-Step Guide together form the main body of knowledge for MindMeld developers.

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Contents

Introduction

• Introduction to Conversational Applications
  • The Rise of the Conversational Interface
  • Machine Learning and the Challenge of Building Useful Conversational Applications
• Different Approaches for Building Conversational Applications
  • Rule-Based Approaches
  • Cloud-Based NLP Services
  • Machine Learning Toolkits
  • Conversational AI Platforms
  • How Good is Good Enough?
• Anatomy of a Conversational AI Interaction
• Introducing MindMeld
  • The MindMeld Philosophy
• Key Concepts

Step-by-Step Guide

• Building a Conversational Interface in 10 Steps
• Step 1: Select the Right Use Case
• Step 2: Script Your Ideal Dialogue Interactions
• Step 3: Define the Domain, Intent, Entity, and Role Hierarchy
  • The Example NLP Model Hierarchy
  • Begin Implementation with MindMeld
• Step 4: Define the Dialogue State Handlers
  • Specify the Superset of Dialogue States
  • Create the Application Container
  • Implement the Dialogue State Handlers
• Step 5: Create the Knowledge Base
• Step 6: Generate Representative Training Data
  • Labeled Query Files
  • Entity Mapping Files
• Step 7: Train the Natural Language Processing Classifiers
  • Domain Classification
  • Intent Classification
  • Entity Recognition
  • Role Classification
  • Entity Resolution
• Step 8: Configure the Language Parser
• Step 9: Optimize Question Answering Performance
  • Proximity-Based Ranking
• Step 10: Deploy Trained Models

Blueprint Applications

• MindMeld Blueprints
  • Before you Continue
  • Quick Start
• Food Ordering
  • 1. The Use Case
  • 2. Example Dialogue Interactions
  • 3. Domain-Intent-Entity Hierarchy
  • 4. Dialogue States
  • 5. Knowledge Base
  • 6. Training Data
  • 7. Training the NLP Classifiers
  • 8. Parser Configuration
  • 9. Using the Question Answerer
  • 10. Testing and Deployment
• Video Discovery
  • 1. The Use Case
  • 2. Example Dialogue Interactions
  • 3. Domain-Intent-Entity Hierarchy
  • 4. Dialogue States
  • 5. Knowledge Base
  • 6. Training Data
  • 7. Training the NLP Classifiers
  • 8. Parser Configuration
  • 9. Using the Question Answerer
  • 10. Testing and Deployment
• Home Assistant
  • 1. The Use Case
  • 2. Example Dialogue Interactions
  • 3. Domain-Intent-Entity Hierarchy
  • 4. Dialogue States
  • 5. Knowledge Base
  • 6. Training Data
  • 7. Training the NLP Classifiers
  • 8. Parser Configuration
  • 9. Using the Question Answerer
  • 10. Testing and Deployment
• HR Assistant
  • 1. The Use Case
  • 2. Example Dialogue Interactions
  • 3. Domain-Intent-Entity Hierarchy
  • 4. Dialogue States
  • 5. Knowledge Base
  • 6. Training Data
  • 7. Training the NLP Classifiers
  • 8. Parser Configuration
  • 9. Using the Question Answerer
  • 10. Testing and Deployment
• Banking Assistant
  • 1. The Use Case
  • 2. Example Dialogue Interactions
  • 3. Domain-Intent-Entity Hierarchy
  • 4. Dialogue States
  • 5. Knowledge Base
  • 6. Training Data
  • 7. Training the NLP Classifiers
  • 8. Parser Configuration
  • 9. Using the Question Answerer
  • 10. Testing and Deployment
• Health Screening
  • 1. The Use Case
  • 2. Example Dialogue Interactions
  • 3. Domain-Intent-Entity Hierarchy
  • 4. Dialogue States
  • 5. Knowledge Base
  • 6. Training Data
  • 7. Training the NLP Classifiers
  • 8. Parser Configuration
  • 9. Using the Question Answerer
  • 10. Testing and Deployment

User Guide

• Getting Started
  • Install MindMeld
  • Begin New Project
  • Upgrade MindMeld
  • Command-Line Interfaces
  • Configure Logging
  • Troubleshooting
  • Environment Variables
• Platform Architecture
  • Natural Language Processor
  • Question Answerer
  • Dialogue Manager
  • Application Manager
• Managing Optional Dependencies
  • Turning off Duckling
  • Turning off ElasticSearch for Entity Resolution
• Working with the Natural Language Processor
  • Instantiate the NLP class
  • Train the NLP pipeline
  • Run the NLP pipeline
  • Evaluate NLP performance
  • Optimize the NLP models
  • Select data for experiments
  • Save models for future use
  • Tracking classifier progress
• Working with the Domain Classifier
  • Access the domain classifier
  • Train the domain classifier
  • Run the domain classifier
  • Evaluate classifier performance
  • Viewing features extracted for classification
  • Inspect features and their importance
  • Save model for future use
  • Using Deep Neural Models for Domain Classification
• Working with the Intent Classifier
  • Access an intent classifier
  • Train an intent classifier
  • Run the intent classifier
  • Evaluate classifier performance
  • Viewing features extracted for classification
  • Inspect features and their importance
  • Save model for future use
  • Using Deep Neural Models for Intent Classification
• Working with the Entity Recognizer
  • System entities and custom entities
  • Access the entity recognizer
  • Train an entity recognizer
  • Run the entity recognizer
  • Evaluate classifier performance
  • Viewing features extracted for entity recognition
  • Save model for future use
  • More about system entities
  • Using Deep Neural Models for Entity Recognition
• Working with the Role Classifier
  • Access a role classifier
  • Train a role classifier
  • Run the role classifier
  • Evaluate classifier performance
  • View features extracted for classification
  • Save model for future use
• Working with the Entity Resolver
  • Understanding the entity resolver
  • Prepare data for the Entity Resolver
  • Train the entity resolver
  • Run the entity resolver
  • Resolver configurations
  • Clean Fitting
  • Consider context-aware entity resolution
• Working with the Language Parser
  • Some parsing terminology
  • Figure out whether your app needs a parser
  • How the configuration instructs the parser
  • Learn how to create a simple parser configuration
  • Define a simple parser configuration
  • Load the parser configuration
  • Run the parser
  • Learn how to create an advanced parser configuration (optional)
  • Experimenting with an advanced configuration (optional)
• Working with User-Defined Features
  • Custom Features File
  • Example of a Query Feature Extractor
  • Example of an Entity Feature Extractor
• Working with the Knowledge Base and Question Answerer
  • Decide Whether your App Needs a Knowledge Base
  • Decide How to Use Question Answering in your Application
  • Prepare your Data for the Knowledge Base
  • Verify that the Data is Clean
  • Import the Data into the Knowledge Base
  • Perform Simple Searches with the get() API
  • Perform Advanced Searches with the build_search() API
  • QuestionAnswerer Configurations
  • Dealing with unstructured text
  • Leveraging semantic embeddings
• Working with the Dialogue Manager
  • Dialogue States
  • Dialogue State Rules
  • Dialogue State Handlers
  • Targeted Dialogue State Handling
  • Using Dynamic Gazetteers
  • Dialogue Flows
  • Automatic Slot Filling for Entities
  • Dialogue Middleware
  • Asynchronous Dialogue State Handlers and Middleware
  • Next Steps
• Working with the Text Preparation Pipeline
  • TextPreparationPipeline Configuration
  • Preprocessing
  • Tokenization
  • Normalization
  • Stemming
• Working with the Auto Annotator
  • Quick Start
  • Using the Auto Annotator
  • Default Auto Annotator: MultiLingual Annotator
  • Auto Annotator Configuration
  • Using the Bootstrap Annotator
  • Creating a Custom Annotator
• Working with Custom Actions
  • Why Custom Actions?
  • OpenAPI Specification for client-server interaction
  • Configure Custom Actions for your MindMeld application
  • A Sample Custom Action Server
  • Using Custom Actions with MindMeld applications
  • Calling Individual Custom Actions inside a MindMeld application
• Active Learning in MindMeld
  • What is Active Learning?
  • Defining the AL config
  • Strategy Tuning
  • Query Selection
  • Quick Reference
• Data Augmentation in MindMeld
  • Quick Start
  • Using the Augmentors
  • Augmentation Configuration
  • English Paraphraser
  • Multi-Lingual Paraphraser
• Deep Neural Networks in MindMeld
  • Domain and Intent classification
  • Entity recognition
  • Addendum
• Internationalization support
  • Setting up language configuration
  • Language stemming
  • System entity resolution
  • Locale-based resolution
• Dealing with Voice Inputs
  • Challenges with speech recognition
  • Phonetic matching in entity resolution
  • Leveraging ASR n-best lists
• DVC & DAGsHub for Model Tracking
  • Available Options in MindMeld
  • Example: Home Assistant Blueprint
• Webex Teams Integration
  • Quick Start
• WhatsApp Integration
  • Quick Start

Walk-Throughs

• Step-by-Step Guide to Active Learning with Log Data in MindMeld
  • Step 1: Setup a MindMeld App with Log Resources
  • Step 2: Define Active Learning Config
  • Step 3: Run Strategy Tuning and Evaluate Hyperparameters
  • Step 4: Select Best Queries

API

• API Reference

MindMeld UI

• MindMeld UI
  • Quick Start

Versions

• Recent Changes
  • MindMeld 4.5
  • MindMeld 4.4
  • MindMeld 4.3
  • MindMeld 4.2
  • MindMeld 4.1
  • MindMeld 4.0
  • MindMeld 3.4
  • MindMeld 3.3
  • MindMeld 3.2
  • MindMeld 3.1
• Package History
  • 4.5.0 (2022-01-10)
  • 4.4.0 (2021-10-18)
  • 4.3.2 (2020-10-15)
  • 4.3.1 (2020-06-17)
  • 4.2.0 (2019-09-16)
  • 4.1.0 (2019-04-22)
  • 4.0.0 (2019-02-25)
  • 3.4.0 (2018-08-20)
  • 3.3.0 (2018-05-10)
  • 3.2.0 (2017-10-23)
  • 3.1.0 (2017-09-20)
  • 3.0.0 (2017-08-14)