1. PAGE1
… or how I learned stopworryingand love the
chatbot framework
formypals at RasaSummit2021
Heather Nolis
MachineLearningEngineer
AI@T-Mobile– @heatherklus

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Heather Nolis
machine learning engineer @T-Mobilesince2017
formerneurosciencePhDhopeful
very activeontwitter@heatherklus
forbetterresponsetime, email meatwork
heather.wensler1@t-mobile.com

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TheTeam
Scope customer care

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Fully StackedTeamfor Real-TimeAI
 Data:
 Data scientists
 Analysts
 Data engineers
 Machine learning engineers
 Software:
 Developers
 Architects
 Ops specialists
 Product:
 Product managers
 Delivery managers
from idea
to deployment
to support ✨

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August 18, 2018

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Weserveover2millioninsightsaday(andgrowing!)
I thinkI forgot to pay my bill 😅
Can I do that now?
Absolutely!
CUSTOMER
T-MOBILE
EXPERT
Flagship product:EXPERT ASSIST
Coach Assist

8. PAGE 8| AI @ T-MOBILE
Neural networks with TensorFlow
A Convolutional Neural Network (CNN) processes initial customer message
and customer data.
Models aredeployed in containers using Kubernetes.
“Unlockmyphone.”
ACCOUNT UNLOCK ORDER
0.80 0.15
0.05
Recent order: YES
CNN

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February 12, 2021
Heather@ Rasa Summit ??

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Some CustomersPreferSelf-Service
One third of care callsopt-in to a bot experience
Messaging care volume continuallyincreases.
More customers prefer messaging each year.
The onlywaywecould trulybelistening toourcustomersis to builda chatbot– forthose whowantit.

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Solets’s makea bot!
Wehaveathatgreat topicmodel….
Let’sjust throwsomething ontopofthat!
Makessense,right?

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RealquickcanI havelike 10new
intents?
They’resuperspecific.
Tensorflow models take thousands and thousands of human created labels.

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A taleof 10 intents, a Self-AssistBotStory
In-HouseTensorflow topic model
88 intents
Hierarchical, defined taxonomy
General topics
Runs on a 10-message window
2,000 utterances per intent (at minimum)
Self-Assist Bot Ask
10 new intents
Overlapping
Highly specific
Runs on a single message
No labeled data
No data labeling support

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Our topic: General Payment
Intent they wanted: Pay My Bill
Things thatare the topic general payment but are not “pay my bill”
I won’t pay my bill because I don’t understand it.
Checking to see if my payment hasgone through.
I want to change my payment method.
If we treated our topics as intents,we risked showing nonsense
responses to customers.

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Shop for a device
vs
Add a line and get a new
device too
vs
Add a line but bring your
old phone
“Iwannabuy aCoolPhone”
“Upgrademy phoneto the CoolPhone
“Buya CoolPhoneformy sister’sline”
“Buya CoolPhoneformy sisterandadd the lineforher”
“Iwanttoadd my sistertomy accountbut Iwantthe CoolPhonebogo”
“Mysisterneedstobeadded tomy accountbut isbringingherown CoolPhone”
“We can’t wait monthsfor you to add new intents.”

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Idea:
Try Rasa
 Why Rasa?
 Solid machinelearning
 open source(wecanchecktheircode)
 uses the sameframework(Tensorflow)as our internaltopic model
 Lesstraining data
 Reuseour custom embeddings
 Extensibleinto further bot functionality
 Problem:
 Time boxedto 4 hours devtime
 (training timenot included)

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4-hour results
Accuracy:
83.1%
F1 Score:
82.1%
Precision:
83.6%
… so now we useRasa

18. PAGE 18 |AI @ T-MOBILE
So what’s different with Rasa?

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The modelsare different…
BespokeTensorflow Topic Model
Runs on a window of messages
2,000 utterances (minimum) to bootstrap an
intent
About 80% accuracy
Rasa NLU Model
Runs on a single message
About 100 utterances to bootstrap
an intent
83.1% accuracy

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…but so isthe pace.
BespokeTensorflow Topic Model
2Yearsin market
Hundreds of production releases
<10 model releases
+2 intents
Rasa NLU Model
5months in market
43 production releases
19 model releases.
+28 intents

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Visibilityleads totrust.
With Rasa X, visibility comes out-of-the-box.
 Immediately review the impact of releases in realtime.
 Allow stakeholders to review conversations, building trust in our
systems.
 Allows stakeholders to suggest improvements directly to mygit
repo– without knowinggit.

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The burdenof initial data is lessened.
Fast intent creation leads to
rapid experimentation.
Intent:Broken
Canreleasesmallintent “stubs”andquickly iteratewithlive
conversationreviews
Reporting available out of the
box.
Topic modelaudit… stillongoing.
ProdAccuracy is King–butcross-validationmetricshelptarget
areasfor incrementalimprovement.

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UX “tiger team” runs parallel scrum
to software.
 Smallerteam allowsforfasterimprovements.
 Productowner
 ConversationDesigner
 Datascientist/machinelearning engineer
 Bottuners:researchnew intents,implement weeklyupgradesto
models
 Rotationalsoftwareengineer
 Allows forcross-trainingonRasamodels
 Createstight cohesionnecessaryforfun,personalbotresponses
withlots ofapiintegrations

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Sowhat’s the impact?
CustomerAssist (aka“Cassie”) took3.4milliondollarsworthofcarecontactssince
ourlaunchin July.
Wehavesolda fewmorechatbotprojects–andhavemultiple chatbotteams.
DatascientiststhroughoutT-Mobileareleveraging Rasamodelstolessen the
burdenofmanuallylabeling data.

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Thank you!
HeatherNolis–MachineLearningEngineer–AI@T-Mobile-@heatherklus
(Special thankyoutoTeamKitt&SMPD!)