The age of touch could soon come to an end. From smartphones and smartwatches, to home devices, in-car systems, touch is no longer the primary user interface. (source: Design News) During this talk, Christophe, Principal Consultant at GraphAware will walk you through the design of building Conversational Bots. To this end, he used Amazon Alexa and combined it with a Natural Language Processing stack backed by a Neo4j Graph Database. You will discover the basics of an Amazon Alexa skill and how the user experience with voice devices can be enhanced with graph based algorithms such as recommendations.

2. Hello humans !

3. Forget everything !

4. Everything you know…

5. Everything you learned…

6. is OVER !!!

7. Welcome to a brand new world…

8. The world of VOICE

9. From Alexa

10. To Google Home,

11. In-car systems,

12. and smartwatches

13. Touch is no longer the primary UI

14. The touch era is OVER !

16. Chatbot and Conversational
Experiences with Amazon
Alexa, Neo4j and
GraphAware NLP
GraphDB Meetup Czech Republic
June 14th, 2017
Christophe Willemsen - @ikwattro

17. Program of tonight
• Chatbots history
• Voice-driven chatbot anatomy
• Let’s build our conference assistant
• Introduction to Natural Language Processing
• Let’s chat..bot

18. Chatbots history
• Alan Turing Test

19. Chatbots history
• Eliza (1966) mimicked human conversations by matching user
prompts to scripted responses. It was able, at least for a time, to
pass the Turing test

20. Chatbots history
• Parry (1972) – Inexplicably simulated a person with paranoid
schyzophrenia. Parry was more advanced than Eliza.

21. Chatbots history
• 1988 – Jabberwacky
• 1992 - Dr. Sbaitso
• 1995 – A.L.I.C.E
• 2001 – Smarterchild
• 2006 – IBM’s Watson
• 2010 – SIRI
• 2012 – Google NOW
• 2016 – Bots for Messenger, TAY
• And Alexa ? Ask her !

22. Chatbot anatomy
• Voice Commands
• Intent detection
• Intent handling
• Response

23. Our conference assistant
• Tell me how many sessions ?
• Tell me how many sessions about a specific topic ?
• Recommend me a session based on my topic preferences
• Find me a session about a specific topic with NLP
Alexa should be able to :

24. Dataset used
Graphconnect London 2017 schedule

25. Voice command
• Give a name to our Alexa skill -> CONFERENCE ASSISTANT
• Define our first intent name (code) -> talksCount
• Define our first utterance

26. ALEXA, ASK CONFERENCE ASSISTANT
(skill invocation)
HOW MANY SESSIONS TODAY ?
(skill utterance)
INTENT DETECTION
SPEECH TO TEXT
TEXT TO SPEECH
LAMBDA FUNCTION
OR
YOUR API SERVER
ALEXA VOICE SERVICE

27. Utterances
• How many sessions ?
• How much sessions ?
• How many talks at the conference ?
• How many talks today ?

28. Intent detection
• 2 main types of detection :
-> Pattern matching (how | count)* many (session | talks) today?
-> Classification machine learning, neural networks, word2vec, ..

29. Alexa send to the skill API

30. Your skill returns a text response

31. Intent detection
• 2 main types of detection :
-> Pattern matching (how | count)* many (session | talks) today?
-> Classification machine learning, neural networks, word2vec, ..

32. First intent test
ALEXA, HOW MANY SESSIONS TODAY ?

33. ALEXA, ASK CONFERENCE ASSISTANT
(skill invocation)
HOW MANY SESSIONS TODAY ?
(skill utterance)
INTENT DETECTION
SPEECH TO TEXT
TEXT TO SPEECH
LAMBDA FUNCTION
OR
YOUR API SERVER
ALEXA VOICE SERVICE

34. Slots
• Variables in the utterances
• Should be filled with possible values
• Slots are sent in the intent request to your API

35. Slots
HOW MANY SESSIONS ABOUT {topic}

36. Second intent test
ALEXA, HOW MANY SESSIONS ABOUT
PERFORMANCE ?

37. Adding personalization
• If you have informations about the attendees and their topic
preferences, you can easily get the sessions in accordance with it.

38. Adding personalization

39. Adding personalization

40. Third intent test
ALEXA, RECOMMEND ME A SESSION

41. GraphAware NLP
• Graph based Natural Language Processing framework
• Information Extraction, Sentiment Analysis, Data enrichment
(ontologies and concepts), similiraty computation, knowledge
enrichment, …
• Beta program, first public open-source version to be released very
soon

43. Enriching with concepts

44. Enriching with concepts

45. Tackling the last intent
• Question / response game
• Treat question as text and process NLP
• Relate question text with Session abstracts texts
• Special type of utterance with only one slot {text}

46. Demo

47. Fun / Ideas with Alexa

48. Resources
• https://github.com/alexylem/jarvis

49. Thank you !
Questions?
graphaware.com
@graph_aware