These slides foverview tools and techniques, which will make it easier to create a chatbot with personality.

1. Building Natural Language
Chatbots

2. What is a Chatbot?
• App which simulates intelligent conversations
• Using rules or AI
• Conversational interface
• Written or spoken text
• Often integrated in messaging platforms
• Slack, Facebook, Messenger, Telegram
• Use cases
• ordering products
• reporting
• managing finances
• internal communications
• scheduling

3. Transforming User Experience
• Language is emerging as the interface to computers
• The ability to add context to conversations makes a more natural way
to talk to a computer
• Gartner: 30% of browsing done by users will turn out to be screenless
in 2020
• Businesses are looking to reach users where they are found –
messaging platforms
• Chatbots make it easier for users to communicate what they want
and to get it
“…messaging is one of the few things that people do more than social
networking” -- Mark Zuckerberg

4. source: BI Intelligence

5. A Quick Look at Voice Assistants
• Amazon, Google, Microsoft and Google have voice assistants
• These automatically handle several common use cases such as voice
search, showing movie trailers, booking restaurant tables, schedule
appointments, etc.
• Others include Facebook M (human assisted) allows customers to do
several things from transferring money to buying a car
• Slack’s slackbots can do things like scheduling meetings, track coworkers’
happiness and take lunch orders among other things
• Businesses which sell or provide customer service through chatbots on
Facebook Messenger (1-800-Flowers, KLM) have seen big increases in sales

6. source: topbots.com

7. source: loyalty-apps.com

8. How Can I Make a Chatbot?
1. Define the scope
2. Figure out the use cases
3. Design the conversation flow and chatbot personality
4. How much intelligence will it need?
5. Choose NLP and/or NLU platform(s)

9. source: liveworld.com

10. Making a Chatbot
• What will the chatbot do?
• Pre-sales?
• Customer service?
• Concierge?
• Data hub?
• Entertainment?
• Learn from user feedback?
• Is it external or internal?
• Both? You might consider 2 separate implementations

11. Specific Use Cases I Settled On
• First implementation would be internal
• It should be able to pull together data from internal sources and
present them in response to conversational queries
• Breaches, incidents, network data summaries, etc
• It should be able to summarize news articles related to the business
• NLP process already analyzes and saves this information
• It should be able to follow conversations in Slack and summarize
• I decided to use a wordcloud for this
• It should have a distinct personality and be able to make small talk

12. The Conversation Flow
• Small talk follows a Request-Response model
• Stateless, immediate response
• Other queries might require multi-step flows, using context
• Context represents a state in a state machine
• There are start and end states based on the conversation flow
• Not necessarily linear
• Use a graphical tool
• Eg. LucidChart, Visio, XMind

13. source: chatbotsmagazine.com

14. Map the Main States

15. The Sad-Face State

16. Happier States

17. The Dream Place State

18. Other Interactions

19. source: cleveroad.com

20. source: Capgemini

21. Level 1
source: Capgemini

22. Level 2
source: Capgemini

23. Level 3
source: Capgemini

24. Understanding Language Technologies
• NLP = Natural Language Processing
• It’s the general term for the set of technologies dealing with natural
language
• NLU = Natural Language Understanding
• Tighter focused subset of NLP which deals with actual conversational
input
• Handle unstructured inputs and convert to a structured machine
understandable form
• Words which the user enters is transformed into intents and entities

25. Requirements for a Development Framework
• Natural Language Processing
• Decomposing input sentences into the constituent parts
• Query generation
• Translating parsed sentences into valid queries
• Context management
• Remembering the results of previous queries to answer futures ones
intelligently
• Natural Language Generation
• Giving the chatbot a personality

26. Development Frameworks
• API.ai -> also known as DialogFlow
• Owned by Google
• Wit.ai
• Facebook
• Microsoft Bot Framework + LUIS
• Watson
• Rasa NLU + Rasa Core (on-prem)

27. Dialogflow Intents and Entities
source: AppCoda

28. Contexts
• When you want your chat bot
to be able to hold a conversation
across multiple interactions, use
contexts

29. source: Medium

30. Small Talk
source: AppCoda

31. Wit.ai
source: Wit.ai

32. Microsoft LUIS

33. Improving Intent Recognition

34. source: Rasa

35. source: Rasa

36. Comparison
DialogFlow Rasa NLU + Core
Mostly complete tool for creating Chatbots 2 separate projects
No end user management No end user management
Robust HTTP API Rasa NLU: HTTP API, Rasa Core: No HTTP API
SDKs: Android, iOS, HTML, JS, .NET, Python, Java, etc Python
Web frontend Trainer UI as a separate project otherwise no GUI
All data is hosted in the cloud, on-prem not possible Everything is local and on-prem
Hosted by Google Self-hosted
Closed source, commercial, enterprise costs Open source

37. Tips and Best Practices
• Consistency: don’t create expectations that the chatbot can’t meet
• Limit responses to less than 3-4 lines of text if possible
• Always design for the “didn’t understand” cases
• Test on different devices
• Embed rich content whenever appropriate, but for consistency across
platforms you may want to minimize
• Buttons/UI components help users respond
• Some personality keeps things interesting