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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Niranjan Hira, Solutions Architect
May 8th 2017
Introducing Amazon LexService for Building Voice or Text Chatbots
Amazon Lex
Why did we build Amazon Lex?
What is Amazon Lex?
How do I build conversational apps using Amazon Lex?
Q&A
Advent of conversational interactions
1st Gen:
Punch Cards & Memory Registers
2nd Gen:
Pointers & Sliders
3nd Gen:
Conversational Interfaces
Developer challenges
Conversational interfaces need to combine a large number of
sophisticated algorithms and technologies
Speech
Recognition Language
Understanding
Business Logic
Disparate
Systems
Authentication
Messaging
platforms
Scale Testing
Security
Availability
Mobile
Complete solution
End to End
Speech to Intent
ASR+NLU
integrated into
one API
Dialog Management
Native support &
maintains context
Text to Speech
Amazon Polly integrated
into API
Business Logic
Native integration with
AWS Lambda
Deployment
One click
deployment
Security
Encrypted data in
transit & at rest
Scale
Completely managed
service
Analytics
Monitor and improve
Text and speech language understanding: powered by the same technology
as Alexa
Enterprise Ready: connect to enterprise systems via SaaS connectors
Versioning and alias support
Build once and deploy to multiple platforms
Designed for builders: efficient and intuitive tools to build
conversations; scales automatically
Amazon Lex - features
Continuous Learning: monitor and improve your bot
Text and speech language understanding
Speech
Recognition
Natural Language
Understanding
Powered by the same deep learning technology as Alexa
Multi-platform
Mobile Messaging Platforms Web IoT
SDKs: iOS & Android
Mobile Hub
Facebook, Twilio
SMS and Slack
SDKs: Java, JavaScript,
Python, CLI, .NET, Ruby on
Rails, PHP, Go, and CPP
Integrated with AWS
IoT via AWS Lambda
Build once and deploy to multiple platforms
AWS Mobile Hub integration
Authenticate users
Analyze user behavior
Store and share media
Synchronize data
More ….Track retention
Conversational botsAmazon LexAWS Mobile SDKs
AWS Mobile Hub
Enterprise SaaS connectors with Mobile Hub
Amazon Lex
Mobile App
Mobile Hub
SaaS connector
Amazon API
Gateway
AWS
Lambda
1: Understand
user intent
Amazon API
Gateway
AWS
Lambda
3: REST
response into
natural language
2: Invoke a SaaS
application or existing
business application
Business
application
Firewall
User inputMobile Hub
custom connector
Versioning and Alias support
AliasVersioning
• Supported for Intents, Slots, and Bots
• Enables multi-developer environment
• Rollback to previous versions
• Deploy different aliases to different platforms
• Run different stacks for dev, stage and prod environments
• Target different user groups with different aliases
v1 v2 v3 latest
v1 dev
v2 stage
v3 prod
Amazon Lex – use cases
Informational BotsChatbots for everyday consumer requests
Application BotsBuild powerful interfaces to mobile applications
• News updates
• Weather information
• Game scores ….
• Book tickets
• Order food
• Manage bank accounts ….
Enterprise Productivity BotsStreamline enterprise work activities and improve efficiencies
• Check sales numbers
• Marketing performance
• Inventory status ….
Internet of Things (IoT) BotsEnable conversational interfaces for device interactions
• Wearables
• Appliances
• Auto ….
Amazon Lex benefits
High quality text and speech language
understanding
Built-in integration with the AWS platform
Seamlessly deploy and scale
Easy to use
Cost effective
Amazon Lex
Utterances
Spoken or typed phrases that invoke
your intent
BookHotel
Intents
An intent performs an action in
response to natural language user
input
Slots
Slots are input data required to fulfill
the intent
Fulfillment
Fulfillment mechanism for your intent
“Book a hotel”
Book Hotel
NYC
“Book a Hotel in
NYC”
Automatic Speech
Recognition
Hotel Booking
New York City
Natural Language
Understanding
Intent/Slot
Model
UtterancesHotel Booking
City New York City
Check In April 19th
Check Out April 21st
“Your hotel is booked for
April 19th”
Amazon PollyConfirmation: “Your hotel
is booked for Nov 30th”
“Can I go ahead
with the booking?
a
in
Slot elicitation
Check In
4/19/2017
City
New York
City
I would like to book a hotel
Sure, which city will you be
traveling to?
New York City
What date do you want to
check in?
April 19th
Conversation context
Slot Values Intents Prompts ConfirmationsSession Attributes
Slot Value
Slot Value
Conversation
Yes/No
Session
Attributes
IntentPrompt
Lex maintains context by storing data throughout the conversation
Confirm
Dynamic conversation flow
ConversationSession
Attributes
Second Intent
Switch Intents
First Intent
Session
Attributes
Conversation
Chain Intents
Takeout
Dine In
Dine In or
Take out?
Anything
else today?Book a Car
Dialog management
I would like to book a hotel
Sure, which city will you be
traveling to?
New York City
What date do you want to
check in?
Tomorrow
… And for how many nights
is this for?
City
Simple Declarative Model
Check-In Date
Check-Out Date
Slots
Which city will you be travelling to?
What date do you want to check in?
How many nights is this for?
Prompts
Build Multi-turn Conversations
Easy Setup in Console
Customize conversations
I would like to book a hotel
Would you prefer to stay in
Downtown this time as well?
Yes
What date do you want to
check in?
April 19th
Sorry no availability. Would
a different location work?
Personalize conversation based
on user preferences
Validate user input and re-prompt
as necessary
Error handling
I want to …. {garbled} …..
Sorry can you please repeat
that?
I am having trouble understanding
Can you please say that again?
Sorry I am not able to assist
you at this time
Clarify by requesting user to repeat
Uses a different prompt every time
Hang up phrase to end the conversation
Rich message formatting
I would like to rent a car
Sure. What type of car are
you looking for?
Compact
• Formatted for messaging platforms
• Multiple cards supported
• Preview capability
• Test in console
Fulfillment
AWS Lambda
Integration
Return to
Client
User input parsed to derive
intents and slot values. Output
returned to client
for further processing
Intents and slots passed
to AWS Lambda function
for business logic
implementation
Save, build and publish
Save Build
Saving your bot
preserves the current
state on the server
Building your bot
creates versions
that you can test
Publish
Publishing your app will create a
version of your bot and provide
an alias to your clients
Test
Test your bot in a
chat window on the
console
Programmatically build bots
… build, test and deploy bots using SDKs
Add Utterances Add Slot Values
SDKs: Java, JavaScript, Python, CLI,
.NET, Ruby on Rails, PHP, Go, and CPP
Pre-defined resources
Built-in Slot Types
AMAZON.DATE
AMAZON.TIME
AMAZON.NUMBER
……
Ready to use slot types that are already trained
with sample values
Do not need to spend the time enumerating sample
values for these slot types
Benefit from continuously expanding pre-defined resources
Amazon Lex pricing
Text Speech
Price per 1000 requests $0.75 $4.00
Free Tier*
(requests per month)10,000 5,000
*Available for the first year upon sign-up to new Amazon Lex customers
Amazon Lex - technology
Amazon Lex
Automatic Speech
Recognition (ASR)
Natural Language
Understanding (NLU)
Same technology that powers Alexa
Amazon Cognito Amazon CloudWatch
AWS Services
ActionAWS Lambda
Authentication
& Visibility
Speech
APILanguage
API
Fulfillment
End-Users
Developers
Console
SDK
Intents,
Slots,
Prompts,
Utterances
Input:
Speech
or Text
Multi-Platform Clients:
Mobile, IoT, Web,
Chat
API
Output:
Speech (via Amazon Polly TTS)
or Text
AWS chatbot challenge
TimelinePrizesChallenge
Start: April 19th 2017
End: July18th 2017
Results: August 11th 2017
Up to $5,000 USD
Up to $2,500 AWS Credits
Tickets to re:Invent 2017
… & more
Register for the AWS Chatbot challenge today!
Build a unique chatbot that helps
solves a problem or adds
value for prospective users.
Amazon Polly: life-like speech service
Converts text
to life-like speech
47 voices 24 languages Low latency,
real time
Fully managed
Amazon Polly: A focus on voice quality &
pronunciation
2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
1. Automatic, Accurate Text Processing
4. Customized Pronunciation
Amazon Polly customers
Media and Entertainment
Education
Accessibility
Content Creation
Telephony/Contact Center
Internet of Things
Gaming
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