ai, nlu, chatbots - aspect · services. what is? activate. whatis_monumentme_connect_se rvices...

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©2017 Aspect Software, Inc. All rights reserved

WHAT NEXT?10 STEPS TO BUILDING YOUR BOT

TOBIAS GOEBEL | DIRECTOR, EMERGING TECHNOLOGIESDR. LISA MICHAUD | DIRECTOR ENGINEERING, NATURAL LANGUAGE

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WHAT IS THE BOT’S ROLE? SET YOUR GOALS

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DIFFERENT KINDS OF CUSTOMER-FACING BOTS

• Marketing• Engage the customer to increase mind share and brand recognition• Goal: enhance market standing and create MQLs

• Sales• Lead to a sale or near-sale situation• Goal: increase the topline revenue

• Service & Support• Traditionally done via IVR, Web• Goal: contact deflection to reduce cost, free up agents for more

meaningful work

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INFORMATIONAL VS TRANSACTIONAL

General Information CRM Information/Transactions

Ente

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CRMDB

Appointment Scheduling

Data Lookup

PaymentsFAQsFAQs

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PICK A CHANNEL

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DIFFERENT CHANNELS HAVE DIFFERENT CHARACTERISTICS

Most personal channel

Concise, 160 characters

Text only

Works on every cell phone

Rich media

Still "new" to some

Requires smartphone

Traditional home for “virtual assistants”

Needs meaningful integration into existing environment

Think hard about the gain

App fatigue won’t go away just because you have chat now

SMS Messenger & Co. Web Chat In-App Chat

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PILOTS AND NEW CHANNELS

• Untapped channel: won’t conflict or compete with any existing programs in your organization

• Starting with a pilot: easier to “sell” internally

• Low-risk commercial engagement, while establishing the technology needed for full roll-out later

• Your team will “forget” that it was ”just” a pilot foundation is laid for moving forward

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CREATE A CONVERSATIONAL ARCHITECTURE

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GRAPHICAL UI VS. CONVERSATIONAL UI

GUI CUIInteraction-Based: User “manipulates” screen Dialog-Based: 2 interlocutors take turnsScreen-Based: new page replaces old Story-Based: dialogue flows in one direction, with

time; previous interaction remains visibleUnambiguous: what you click is what you get Error-Prone: misunderstandings, ambiguities,

corrections, confirmations, …“High Bandwidth”: visual channel allows higher information throughput

“Low Bandwidth”: gradual information discovery

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THE PECULIAR PRONOUN…

• CUIs use conversational markers to efficiently refer to previously mentioned concepts

What is the Concierge Service?

A premium service with exclusive information and assistance. It includes point of interest

search, restaurant/hotel reservations, cultural event schedules, and more…

How much is it?

How much is what?

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GUIS ARE STRUCTURED THROUGH SITE MAPS, OR INFORMATION ARCHITECTURES (IA)

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CUIS NEED A CONVERSATIONAL ARCHITECTURE (CA)

• Define the scope: Start by collecting the top N questions

• Gather your content

• Define hierarchies within content, and contextual dependencies• For every concept introduced in an answer, expect a follow-up question…

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= answer mentions concepts/terms that user might have follow-up questions on, and therefore needs to remain the context for resolving anaphora

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DESIGN DIALOGUE FLOW AND STORYBOARDS

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DETAILED FLOW AND MESSAGE DESIGN

https://www.aspect.com/globalassets/microsite/nlu-lab /images/ChatbotDesignTemplate.pdf

https://www.aspect.com/globalassets/microsite/nlu-lab/images/ChatbotDesignTemplate.pdf

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THE FIRST RULE OF MESSAGE DESIGN

For every message you design, anticipateevery possible way someone could respond

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SOME TIPS…

o Design for how humans talk, not how you WANT them to talk

o Design with context in mind

o Don’t expect your user to know exactly what they want

o Don’t use rhetorical questions

…and savings?

Checkout

That’s not what I meant vs. go back

Battery

Interested in our newsletter? Click here:

aspct.co/Fg45Si9

Where can I get one?

Yes!

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DESIGN THE INTEGRATIONS

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REUSE EXISTING WORK

• Your IVR, Web, mobile presence should already be built on Web Services, exposing similar if not same functions

• FAQs: Integrate with KB, or start with custom data base/structure

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COLLECT CHAT DATA

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VARIATIONS ON A QUESTION

Start with your top N questions:

Q: “How do I free up space?”

A: If an update fails because you need more space, you can

manually remove content from your device by clicking on ‘Settings.’

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VARIATIONS ON A QUESTION"I don't have enough space to update my iphone"

"It says I have not enough space on my device"

"I don't have enough room for the update"

"I need more space"

"How do I free up space?"

"My memory is full"

"My disk is full"

"I ran out of space"

"The space is insufficient"

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• Age

• Region

• Socioeconomic group

• Language background

• Investment in the task

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CROWD-SOURCING THE COLLECTION

Internal “Users” Amazon Mechanical Turk

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CROWD-SOURCING THE COLLECTION

ANSWERS:

• How do I remove files?

• How can I delete files?

• How to remove files

• Removing files

PROVIDE VARIATIONS TO:

How do I delete files?

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CROWD-SOURCING THE COLLECTIONMEMORY

MANAGEMENT

$400

If an update fails because you need

more space, you can manually remove

content from your device by clicking on

‘Settings.’

“I need more space”

“How do I free up space?”

“My memory is full”

“my update failed because of space”

“How do I remove content from the phone”

“Where do I go to clear out my device?”

“JEOPARDY” APPROACH

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PICK A PLATFORM AND DEVELOPMENT APPROACH

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TASKS FOR THE BOT

Task #1: determine an INTENT

I want to book a stay.

Can I check my reservation?

Task #2: extract DATA

Check-in on Friday February 5

I want a king-sized bed

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transferdata

transfer data

insert object

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A TOP-DOWN VS. BOTTOM-UP APPROACH

RULES

DATA

A machine-learning approach requires a large amount of classified data from which to derive a classifier that can classify new data the same way. It relies on that data capturing the diversity one expects to see in future data.

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A TOP-DOWN VS. BOTTOM-UP APPROACH

RULES

DATA

A linguistic approach starts with intuitions about how we should classify the input and leverages a full linguistic analysis to empower the classifications. It can then be augmented and tuned when more data exist.

Top-down

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Botto

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n• A Big Data approach –

requires large amounts of classified data.

• Relies on that data capturing the diversity one expects to see in future data.

• Result: a “black box” classifier.

• When an intent is misclassified, you need to add training data to convincethe classifier to do otherwise.

• A Sparse Data approach –does not require as much data to get started.

• Leverages linguistic abstractions to capture expected diversity.

• Result: a fully inspectable classifier.

• When an intent is misclassified, you can directly modify the logic to tell it to do otherwise.

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SEMANTIC APPROACHES TO INTENT CLASSIFICATION

I want to transfer my data

How do I move my files?

I want to transfer that to my file

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IMPLEMENT THE DIALOGUE

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"I don't have enough space to update my iphone”,DELETE"It says I have not enough space on my device”, DELETE"I don't have enough room for the update”, DELETE"I need more space”, DELETE"How do I free up space”, DELETE"My memory is full”, DELETE"My disk is full”, DELETE"I ran out of space”, DELETE"The space is insufficient”, DELETE

IMPLEMENTING THE CHATBOT: MACHINE LEARNING

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IMPLEMENTING THE CHATBOT: CLASSIFICATION RULES"I don't have enough space to update my iphone"



"I need more space"

"How do I free up space"

"My memory is full"

"My disk is full"



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IMPLEMENTING THE CHATBOT: CLASSIFICATION RULES"I don't have enough space to update my iphone"



"I need more space"

"How do I free up space"

"My memory is full"

"My disk is full"



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INTERNAL TESTING AND REVISION OF YOUR USE CASE DETECTION

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INTERNAL TESTING AND REVISION

Collect Example Sentences

EngineerNLU

Test NLU

Identify Sources of Error

Internal Testers

QA Service

Actual Users

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EARLY DEPLOYMENT AND REVISIONS

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EARLY DEPLOYMENT AND REVISIONS

Collect Example Sentences

EngineerNLU

Test NLU

Identify Sources of Error

What percentage of incoming requests are in-scope?Do you need to add more use cases?

How many use cases can be handled without human assistance?Does the intent classification need additional work?Is the response text being understood clearly?

What are the most common use cases?What language patterns are you observing?How can these observations help future projects?

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THANK [email protected] / @[email protected] / @LISANMICHAUD

NLU Lab: www.aspect.ai

mailto:[email protected]

mailto:[email protected]

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