Messaging apps have become more popular ways for people to communicate than social networks or phone calls. As a result, chatbots are growing in usefulness, especially for customer service tasks. There are different types of chatbots, from task-oriented bots to predictive, data-driven bots. Current chatbots have capabilities for intent recognition, entity recognition, and sentiment analysis using machine learning, but still face challenges with ambiguity. Future chatbots may be able to pass the Turing Test by more closely resembling human conversations. Oracle's Intelligent Bot platform includes components for natural language processing, customization of bot flows, and integration with backend systems.

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Lyudmil Pelov
@lpelov
Oracle A-Team
March, 2017
IMPLEMENTING CHATBOT
PLATFORMS

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• We Message!
– there are more people on messaging apps
than social networks
– millennials are texting more than calling
– teens would rather message than have a
phone conversation
– 65% of consumers want to message a
business to*
– > 50% of all consumers would make a
purchase via a message
– messaging is just easier, faster, and more
productive for targeted tasks than mobile or
web
*Based on survey conducted by Smooch.ai

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• most common types of bots
• highly specialized and structured
interactions
• typically user initiated
• integrated with enterprise systems
• useful in the support or service
category
Task Oriented Data Driven & Predictive
• personalized based on profile, past
behavior and contextual information
• monitors data and initiates
conversation when data crosses
threshold
• predictive Intelligence based on data -
key for bots to be a useful in the long
run across use-case categories
First Generation Chatbots

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Next Generation Chatbots
“non supervised general availability bots”
“sophisticated prediction models requiring reduced supervising”
“auto labeling”
“anomaly detection”
“concept extraction”
“augmentation argument (capable to argument based on facts)”
“semi-supervised named entity recognition”

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Future Generation Chatbots
“be able to take successful the Turing test?!”
“Turing Test: If the evaluator cannot reliably tell the machine from the human (Turing originally suggested
that the machine would convince a human 30% of the time after five minutes of conversation) , the
machine is said to have passed the test.”
“The test does not check the ability to give correct answers to questions, only how closely answers
resemble those a human would give.”
Probably not likely to happen soon but not impossible as the ML models get more advance!
https://en.wikipedia.org/wiki/Turing_test

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FIRST GENERATION CHATBOT CAPABILITIES AND CHALLENGES

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Intent Recognition (classification)
“the ability to understand the intent from human ambiguous language”
#wordvector, #deeplearning4j, #opennlp, #stanfordnlp

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Advance Intent Recognition Challenge
“The city councilmen refused the demonstrators a permit because they feared violence”
“What does the word “they” refer to here — the councilmen or the demonstrators? What if instead of
“feared” we wrote “advocated”?”(1)
“Oren Etzioni: When AI can’t determine what “it” refers to in a sentence, it’s hard to believe that it will
take over the world”(1)
https://techcrunch.com/2017/02/25/conversational-ai-and-the-road-ahead/ (1)

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Intent Recognition Training Model Guideline
“intents should be different enough to help the ML disambiguate better”
“Good Pizza vs Bad Pizza are not two different intents, the intent here would be Pizza Quality, Good and
Bad would be entity Rating”
“single keywords are not useful”
“unique sentences are super helpful, semantically and synonym rich”
“do {word} sentence construction in different intent models will falsely give high prediction score for all
intents”

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Entity Recognition (NER)
“the ability to automatically recognize entity meanings from ambiguous language”
“despite all improvements in ML, the problem of accurate entity recognition is far from been solved”
“main effort currently is directed to find approaches to reduce the annotation labor – semi-supervised
NER”
#opennlp, #stanfordnlp

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Entity Recognition (NER) – example with OpenNLP
Model
“The other day at work I saw <START:person> Larry Elison <END> at <START:building> HQ600 <END>”
Test
“The other day at work I saw Lyudmil Pelov at HQ300”
“The other day at work I saw lyudmil Pelov at 300”

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Sentiment Analysis (emotional intelligence)
“evaluate subjective patterns and expressions of sentiment within textual content”
“usually identifies if expression was positive, negative, mixed, or neutral”
“requires a high degree of supervised ML on big data corpus, usually Twitter”
#deeplearning4j, #opennlp, #stanfordnlp

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Probabilistic Parser
“probabilistic parsing is using dynamic programming algorithms to compute the most likely parse(s) of a
given sentence, given a statistical model of the syntactic structure of a language”
“parse any address, name, number, date or any unstructured text in useful components”
#stanford, #duckling, #parserator
http://nlp.stanford.edu/projects/stat-parsing.shtml

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Probabilistic Parser – Duckling Example
#stanford, #duckling, #parserator
https://duckling.wit.ai/

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CHATBOT CONCEPTS

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https://www.behance.net/gallery/47419375/Chatbot-Powerful-UX-Concepts

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ORACLE INTELLIGENT BOT - KEY COMPONENTS

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• active machine
learning
• NER
• sentiment recognition
• SDK to consume back-end
API’s
• declarative bot flow
• state machine context driven
workflows
• cross channel chat interface
Channel
Configurator
Dialog Flow
Execution
NLP Engine
Custom
Components
in Dialog
Flow

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ORACLE INTELLIGENT BOT – HOW IT WORKS

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Design-time
“specify intents and entities”
“test intents and flows”
“create a flow (depicts bot execution logic)”
“wire up the flow to clients and custom components (channels & back-end API integration)”

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Run-time
“incoming message from channel activates bot flow”
“intent classification: determines what the user request is most likely trying to accomplish based on
machine learning”
“entity resolution: the entity recognizer identifies the important words and phrases contained in each user
request”
“dialog engine execution: once the entity’s are resolved, the dialog engine or manager formulates the
response to return to the user based on a state machine”

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ORACLE IBCS: PaaS Multi-Tenant Architecture

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23
Custom
Components
Customer MCS/
Customer ACCS/
Customer JCS
LBaaS/
Cloudgate
ACCS Container C
Dialog engine
OEHPCS Service
Intent Server
ACCS Container D
ACCS Container A
Channel
Drivers/Auth
UI Endpoints
API
ACCS Container B
DBaaS (1 per tenant)