by Pascal van Kooten at PyData Barcelona 2017 Description: The DeepCare chatbot is capable of learning to answer customer questions. Using a hybrid approach of NLP and Deep Learning, it tries to combat logical fallacies that occur in pure deep learning bots, while still coming up with unique answers. A live demo will be available at http://deepcare.online on the day of the talk.

1. DeepCare Chatbot
Generating answers to customers using a hybrid approach
of
Deep Learning and NLP
Pascal van Kooten

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Introduction
● First conference; first talk
● MSc Methods & Statistics, Utrecht University
● Innovating @ Jibes Data Analytics for ~3 years
● Working for companies to investigate:
– Blockchain
– Computer Vision
– Machine Learning / NLP
● Human Machine Interaction

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Gartner’s Hype Cycle 2016

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And this is where you can find me...

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Fun projects
● whereami
Uses WiFi signals and machine learning to predict where you are
● deep_eye2mouse
Move the mouse by your webcam + gaze
● sky
Using machine intelligence for web crawling
● Neural Complete (presentation in 2 weeks in Moscow)
Neural network to auto complete neural network code
● And…. DeepCare
github.com/kootenpv

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Chatbots

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Chatbots

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Chatbots
● These chatbots take the initiative
● AT&T experimented with chatbots, but cannot
see one online
● Fun experiment to have a reactive chatbot

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Data
● Find “big QA data” in the English language
● @ATTCares, 1.3M tweets as of May 2017
● 100k Q&A collected through twitter api
● ~15k related to “wanting/needing iphone”

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Problem
● They receive ~3000 tweets per day
● Corporations want an automated solution
● Very versatile problems:
– Information requests
– Complaints
– Ask for change in system
● Bot needs to detect when it should be quiet
● First task: “social selling”

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Goal
● Without business constraints…
● Try to see if a bot can reliably reply like a human
● Combine NLP and Deep Learning
– NLP for transparency, answer yes/no
– Deep learning for variation in response

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http://deepcare.online
(available during PyData)

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Examples (1/2)
Q: I want an iphone for xmas
A: santa heard you, pvkooten order an iphone at att.com/search?
q=iphone & get #rolloverdata asap. ^fakeAT&Tbot t.co/J4c8G3KR1S
Q: can't decide if I want an iphone 7 or samsung note 4
A: we have great deals on both! get both this your lifestyle! check them
out here att.com/search?q=iphone 7 ^fakeAT&Tbot
Q: I want to see a movie
A: I'm not sure if an iphone is what you really want. I can only help you if
you want an iphone.

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Examples (2/2)
Q: I want to sell an iphone
A: I'm not sure if an iphone is what you really want. I can only help you if you want an
iphone.
Q: I want to buy an iphone
A: we have got what you need, pvkooten grab an iphone w/ #rolloverdata @ att.com/search?
q=iphone today! t.co/J4c8G3KR1S
Q: I need a charger for my iphone
A: we have one for you, pvkooten get a new charger & many other code tweet-@
att.com/search?q=iphone charger for 20%!^fakeAT&Tbot
Q: i want a case for my iphone 6
A: we have got you covered, pvkooten get a fab case @ att.com/search?q=iphone 6 case
%20cover%20 g & dm me for $$$ off! ^fakeAT&Tbot t.co/J4c8G3KR1S

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It’s not only deep learning
● Logical mistake:
Q: Stop my samsung contract
A: Ok, I stopped your iphone contract

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Overview
● Pre-processing
– Generic / Twitter related
– Domain specific
● Phase 1: NLP Model
● Phase 2: Deep Learning answer generation
● Post-processing

17. Preprocessing
Pascal van Kooten

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Case study
Q: i want a case for my iphone 6
A: we have got you covered, pvkooten get a fab case @
att.com/search?q=iphone 6 case%20cover%20 g & dm me for $
$$ off! ^fakeAT&Tbot t.co/J4c8G3KR1S
is seen after the preprocessing step as:
Q: i want a case for my PRODUCT
A: we have got you covered, NAME get a fab case @
OFFER_LINK & dm me for MONEY_AMOUNT off! INITIALS
WEBCARE_LINK

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Generic preprocessing
● Name (API provides actual name) NAME→
● Employee name+time AVAILABILITY→
● Tags (@something) TAG→
● Links LINK (experimented)→
● Emoticons

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Specific preprocessing
● Brand name
● Products
● Competitors

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Preprocessing
● Preprocessing to help generalize
● Only preprocess things that can be reused in the
domain
● Plus a crucial component to a company: PRODUCT
● ….

22. NLP Model
Restrict answering
Pascal van Kooten

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Traditional NLP
● Depending on language model
– Lemmatization (cats → cat)
– Part of Speech (cat → NOUN)
– Dependency tree (I → subject)
✓Allows generalization!
✗Depends on language model getting it right
✗Feature explosion makes it difficult to generalize
✗Creating rules can be time consuming
✓ It’s very transparant
✓ Rules can give guarantees deep learning can’t

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Model explanation
● spaCy (https://spacy.io/)
● Rule uses:
– Part of Speech
– Lemma
– Negation
For a sentence,
- need/want... is the lemmatized verb
- which has PRODUCT as a nearby child
- but no negation dependency on the same level or above as PRODUCT
- the subject is “I”
- ???

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Case study
Q: i want a case for my iphone 6
A: we have got you covered, pvkooten get a fab case @ att.com/search?q=iphone
6 case%20cover%20 g & dm me for $$$ off! ^fakeAT&Tbot t.co/J4c8G3KR1S
want (VERB, ROOT) ✓
---- I (PRON, nsubj) ✓
---- case (NOUN, dobj)
-------- a (DET, det)
-------- for (ADP, prep)
------------ iphone (NOUN, pobj) ✓
---------------- my (ADJ, poss)
---------------- 6 (NUM, nummod)

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Examples
want galaxy not iphone
not iphone ✓ want iphone!

27. Generative model
Generating answers
Pascal van Kooten

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Generative neural network model
● Popularized by Google’s seq2seq:
A general-purpose encoder-decoder framework for Tensorflow that can be
used for Machine Translation, Text Summarization, Conversational Modeling,
Image Captioning, and more.
● Map sequence of words to another sequence of words
● Chinese sentence to English sentence
● Given training examples, minimize loss
● Can also be used for creating a chatbot
● Most likely a variation on this model is being used by Google
Translate

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Generative neural network model

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Generative neural network model
https://indico.io/blog/sequence-modeling-neuralnets-part1/

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Code
● tensorflow
● Started generative model code from
Conchylicultor/DeepQA

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Neural network parameters
● batch_size: 128
● glob_step: 3186
● max_length: 25
● learning_rate: 0.001
● embedding_size: 128
● hidden_size: 512
● num_layers: 2
---------------------
loss: 0.06
time: 24 hours

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Conclusion
●
It makes small grammar mistakes
●
It can still make mistakes in logic
– Mostly with unseen data
● Not optimal because 2 models, but fun experiment
● Like OpenAI….
●
Shows signs of sentiment:
Q: I want an iphone but my mom says iphones are the devil
A: do not get upset! order an iphone at att.com/search?q=iphone iphones
with…

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Questions?