1. Journey to the Mysterious World of Chatbots
Deepti Tiwari & Manju Joseph
February 2019

2. Agenda Advancements in AI & Chatbots NextGen Interactions Taking Stock
Scope of Pilot
Chatbot Architecture
Key Design Considerations
Pilot Results
Agenda

3. Advancements in AI & Chatbots
Buzzword of the decade
Equipped with NLP and ML technologies
Human-like interactions
AI-powered chatbots
Disruption potential
Every decade seems to have its technology buzzword: we had PCs in 80s; Internet and worldwide web in 90s; smart phones and social media in 2000s; Artificial Intelligence (AI) and Machine Learning are the buzzwords in this decade.
Over the past several years, there has been a growing belief that AI is a limitless, mystical force that will soon be able to supersede humans and solve any problem.
ML and deep learning technologies are increasingly doing things only humans were able to do. Machines have become much better at handling data and learning from it than we are. Advancements in NLP technology has made it possible to build conversational interfaces that can engage in intelligent and natural conversations with humans.
Technology experts have identified few niche areas where AI can touch human lives – using AI to help improve patient health outcomes, enhance customers’ in-store experiences in retail industry, image recognition and voice translation systems, to name a few.
Conversational platforms were named as one of 10 strategic technology trends for 2018 by Gartner. AI has made chatbots more lifelike, and they are becoming pervasive. AI chatbots are disrupting many industries; studies show that 80% will use chatbots by 2020.

4. NextGen Interactions
The next generation interactions are evolving to deliver intuitive & seamless user experience for tomorrow’s organizations.
For a long time, we’ve thought of interfaces strictly in a visual sense: buttons, dropdown lists, and so on. But now we are staring into a future composed not just of visual interfaces, but also of conversational ones. Conversational interfaces can take two forms – the intelligent voice assistants that allows you to talk (like Alexa or Siri) OR chatbots that allow you to type.
Audio based interaction - through touch/voice (for example, when you make a customer care call through your phone), through conversational interfaces (for example, Google Home, Siri, Alexa)
Visual based - through gestures, motion, facial expressions (for example, gaming environment like Xbox, PS3) and AR/VR/MR technologies
Sensor based - through dynamic human interactions such as touch, emotions, human mind cognition (psychology, neuroscience). Breakthrough technologies like BCI (brain-computer interface) allows for 2-way communication between a wired brain and an external device. Haptic technology recreates the sense of touch by applying forces, vibrations, or motions to the user (for example, sensation you get through console game controllers).

5. Taking Stock Accessing large volumes of data
Most chatbots are built to do simple things
Analyzing context to provide intelligent answers is still not a reality
Content everywhere – user docs, marketing, support
Content overlap across different products
Content not easily searchable
Complication
Huge volumes of structured and unstructured content that had to be curated
Position
Engage with internal and external technology experts to build, test, and train an AI-infused chatbot
Benefit
Maximize customer satisfaction by providing personalized and value-added services
Action
Chatbot pilot implementation for few of our flagship products
Situation
Use AI to derive transformative insights from a wealth of enterprise content
Implication
Accuracy and value of response is critical
With this quick overview of AI and its potential impact, we’d like to now zoom in to what we set out to do. We, the doc team at Cisco, wanted to be the early adopters of this disruption and understand what it takes to build a reliable conversational interface (aka chatbot) that specializes in specific tasks, provides rapid, contextual responses and recommendations to the users.
So, we started by taking stock of where we are and what lies ahead:
Situation – We had a wealth of enterprise content (such as product docs, marketing collaterals, support articles) and we wanted to use AI to derive transformative insights from this. Content that was overlapping across different products, content was not easily searchable.
Complication – We knew it was complex to handle huge volumes of structured and unstructured content that had to be curated.
Implication – We were certainly aware that accuracy and value of response is extremely critical, which could make or break the customer experience.
Position – We engaged with internal and external technology experts to build, test, and train an AI-infused chatbot.
Action – We wanted to test the waters first by starting small and executing a chatbot pilot for few of our flagship products.
Benefit – Our end goal was to maximize customer satisfaction by providing personalized and value-added services.
With so much of content out there, short crisp precise responses…..aligning to NextGen expectations and give them more superior experiences - personalized service to delight customers.

6. Scope of Pilot Start small and scale later
Host the chatbot on Cisco’s doc portal
Key chatbot features:
Accuracy
Conversational maturity
Autonomous reasoning
Scope of Pilot
With these challenges in mind, we limited the scope of our pilot project to cover only 2 of Cisco’s flagship products. The aim was to build a chatbot that can be hosted on our documentation portal.
Key features of the chatbot include:
Accuracy
understand brand-specific or industry-specific knowledge and terms
Conversational maturity
provide an accurate first response, and also propose options to confirm or clarify intent
Autonomous Reasoning
perform complex reasoning without human intervention

7. Chatbot Workflow NLU/NLP Layer Conversational System
Machine Learning
Feature Engineering
Preprocessing
PoS Tagging
Hi, how may I help you today?
Where will the future Olympics be held?
Conversational System
Dialogue Manager
Did you mean Summer Olympics?
Yes
Tokyo will host 2020 Summer Olympics.
Information Extraction
Deep Learning
Response
Generation

8. Key Design Considerations
NLP
Context Awareness
Key Design Considerations
Marketing
PSIRTS
TAC
CDETS
TechDocs
FAQs
Training the Bot
Knowledge Ecosystem
Let me tell you about the key elements that we considered while designing our chatbot framework.
Let’s now review the key design elements that played an important role in our chatbot implementation.
Context Awareness - Chatbots we interact with today may be built to do simple things, but our goal was to leverage advanced machine learning technologies and build an intelligent bot that is capable of interacting with users in more contextually relevant ways.
NLP - NLP gives a chatbot the ability to learn and mimic the styles and patterns of human conversation.
Information extraction - Curated content is the fuel of AI, which in turn enables more effective interactions. 
Training the chatbot – Intelligent chatbots that can discover new patterns and get smarter as they encounter more situations.
Personalization - Personalize the responses rather than provide a generalized set of responses
Let’s go through each of these components in detail…
Personalization

9. Understanding the Context
Taxonomy
Metadata
Understanding the context is a key element in a chatbot model. Chatbots work on entities, intents, and responses.
For example, if the content is structured, has metadata, and uses a taxonomy, these will help the chatbot know which piece of information to serve the user. This includes common metadata such as product, version, technology, user role, and operating system. 
We, technical communicators can start by making sure our content is in a structured format suited to machine learning. For example, in a Help topic, the machine can easily identify the subject (and map this to an Entity), the problem or goal (and map this to the Intent), and the steps in a task (and map this to the response). 
If the chatbot can also draw on contextual information (such as the user's purchase history, their previous support calls, and their location), it will be able to give a more targeted response, in comparison to a user searching by keywords. 
Hence, with the right strategy, taxonomy, and semantic delivery mechanism, you can feed the bots efficiently and effectively and serve up those personalized answers, building engagement with your customers in the process. 

10. Natural Language Processing
How do I configure DHCP server for zero-touch provisioning in Cat 9K?
“How” “do” “I” “configure” “DHCP” “server” “for” “zero-touch” “provisioning” “in” “Cat” “9K”?
Intent
Entity
Type: Technology/Feature
Entity
Type: Product
Challenges
Acronyms
Domain Terminology
Special Characters
Rich Text
Pre-processing: Tokenization | PoS Tagging | Stopwords
Entity Tagging: Identifying concept
Intent Identification: Action to be taken by chatbot
Context: Word Embedding Model
Three common challenges with NLP are natural language understanding, information extraction, and natural language generation. 
Whenever a user asks a question in the bot window, the first process that gets initiated is Pre-processing the query.
Pre-processing includes tasks like:
Tokenization: A process of breaking a stream of text into words.
PoS (Part of Speech) Tagging: PoS tagging is about assigning parts of speech to each word, such as noun, verb, adjective, etc.
Stopwords are commonly used word (such as “the”, “a”, “an”, “in”) that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query.
Then comes the Feature Engineering.
Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work.
General Vocabulary is obtained using Wikipedia. 
Domain Vocabulary is generated using Word Embedding model trained on existing data available within Cisco documentation, books and TAC SRs.
If feature engineering is done correctly, it increases the predictive power of machine learning algorithms by creating features from raw data that help facilitate the machine learning process.
Next is NLU Process.
NLU process has 3 specific concepts like:
Entities, which represent a concept in your Chatbot. For example, a payment system in your Ecommerce Chatbot.
Intents are the actions chatbot should perform when the user say something. For example, I want to buy a router, here intent is to buy something.
Context: Initially when NLU algorithm analyzes a sentence, it does not have the history of the user conversation. It means that if it receives the answer to a question it has just asked, it will not remember the question. For differentiating the phases during the chat conversation, it’s state should be stored. It can either be flags like “Ordering Pizza” or parameters like “Restaurant: ‘Dominos’”. With context, you can easily relate intents with no need to know what was the previous question.
Final step is Information Retrieval.
Top N documents are retrieved for the user query using TF-IDF and Word Embedding Matrix and top ranked result is presented as the answer.
Then the Deep Learning Model extracts the top N documents for the user query using NER, LSTM algorithms and Word Embedding Matrix and top ranked results .
Further, based on user’s feedback, the learning model trains the bot and help refining the results for next responses.
Tensor Flow Model Manager is all about managing and processing info from knowledge graphs.
Tensor flow Model is a Google-developed open source library for machine learning and deep neural networks research, used for numerical computation using data-flow graphs.

11. Training the Chatbot Knowledge Base Creation Reinforcement Learning
Ingest raw data
Generate domain vocabulary
Reinforcement Learning
Commonly used acronyms
Greeting statements
Answering weird questions
Approach for unresolved queries
Pre-defined set of answers
Establish dialogues
Capturing user experience
Feedback logs (Thumbs up/down)
Chat closure analysis
Chatbots are only as intelligent as the knowledge they have access to. Collecting training data used to train machine learning classifiers or building corpuses of data is critical to achieving human-like interactions.
Most of the chatbot frameworks have in-built general vocabulary obtained from Wikipedia or from a university DB. But domain vocabulary is crucial and specific to your domain.
Machine learning lets bots develop a growing set of knowledge and understanding. Analytics from the conversations allow bot builders to spot where the bot has difficulties correctly analyzing sentences and, in addition to the automated machine learning.
Determining best approach for unresolved queries:
Another important factor is the course of action to take when users don’t provide expected responses or if the chatbot is unable to understand or resolve the intent of users’ context or conversation. Because repeatedly asking users to rephrase a question or try again would be a sure path to frustration, the chatbot needs to demonstrate a partial understanding to reveal which part of the question it doesn’t understand.
A final set of data can come from customer satisfaction scores at the end of each chat. Record Feedback and study logs.
Did user end the chat with a satisfied answer (with thumbs-up), not satisfied with answer, or bot couldn’t deliver an answer(gave standard response)?

12. Personalization Understanding an individual over a period of time
User’s profile information
Area of interests (products, technologies, doc types, tasks, install base)
Improve first-contact resolution
Chat history
Current context
Ability to predict the right options
Minimal conversational interaction
Meaningful predictions
This leads to one of the most important design decisions: How much personalization can be brought into the interaction itself?
User’s profile information can be used for:
- greeting the user
- to get some background about the user, for example, internal/external/partner user, department he belongs to, install base, etc.
For instance, can the system remember user profiles, previous interactions, the interactions of other users in the system, the current context and the environmental know-how? Learning the nuances of the user’s requests help improve the first-contact resolution.
The goal is to decipher the problem with the least number of questions and manual effort from users, which requires an emphasis on understanding the user’s background and the context of his or her past and present interaction.

13. Knowledge Ecosystem
Generate new knowledge to handle most common queries
Leverage data from variety of content sources
TechDocs, Marketing, Bugs, Security Incidents, Technical Support, FAQs
Handle both structured and non-structured content
TechDocs content vs other content
Extract, Refine, Process non-structured content
Marketing
PSIRTS
TechDocs
TAC
FAQs
CDETS
Content could be structured or non-structured. Increased focus should be on the underlying data ecosystem, including the ability to pull and leverage data from a variety of sources, both within and outside the enterprise. Having a layer that can extract, refine, and process data should be factored into the overall solution.
Capability to link data from all the available sources and create and leverage graph databases. The faster the chatbot accesses, processes and responds to data, the better the overall experience will be.

14. Chatbot Pilot Results Iteration 1 Data Input Iteration 2 2 7000 XML
Accuracy
Data Input
Conversation 2
7000
XML
XML
Iteration 2
Platform Series
9500 & 9300
Topics and
Contents
Data Format
Accuracy
Conversation
Reasoning

15. “Moving from pilot to large-scale implementations of AI-chatbot is a daunting task that requires sound planning”
Your organization needs to gain experience of what AI can, and cannot, do, and what business value it can deliver.
Planning, budget, skillset

16. Demo