![The Good of Chatbots
Message platforms are everywhere
Simple, known interface
Applicable to many situations
63% of the people are willing to communicate through chatbots[1]
[1]: https://venturebeat.com/2017/06/17/the-good-the-bad-and-the-ugly-of-chatbots/
avaamo.com](https://image.slidesharecdn.com/chatbots-201712-171220163052/85/Chatbots-4-320.jpg)
![Introduction: the Bad of Chatbots
73% won’t use the chatbot again after a bad experience[1]
75% want to know it is a bot[1]
50% are disturbed when a bot pretends to be human[1]
Not all languages are equally supported
[1]: https://venturebeat.com/2017/06/17/the-good-the-bad-and-the-ugly-of-chatbots/
steemit.com/@resteemitnow](https://image.slidesharecdn.com/chatbots-201712-171220163052/85/Chatbots-5-320.jpg)
Uploaded byVectr.Consulting
Chatbots
The document discusses chatbots, highlighting their benefits, such as user accessibility and natural language interfaces, along with their drawbacks, including user dissatisfaction after poor experiences. Various types of chatbots and their functionalities are detailed, alongside key concepts such as intents, entities, and conversation context. The future of chatbots is projected to focus on improving natural language understanding (NLU) speed and accuracy, with increasing acceptance and usage in businesses.
Chatbots
- 1.
- 2. Introduction Chatbots provide anatural language interface to their users
- 3. Eugene Goostman Google AIchatbot Facebook AI develops own language Microsoft’s Tay
- 4. The Good ofChatbots Message platforms are everywhere Simple, known interface Applicable to many situations 63% of the people are willing to communicate through chatbots[1] [1]: https://venturebeat.com/2017/06/17/the-good-the-bad-and-the-ugly-of-chatbots/ avaamo.com
- 5. Introduction: the Badof Chatbots 73% won’t use the chatbot again after a bad experience[1] 75% want to know it is a bot[1] 50% are disturbed when a bot pretends to be human[1] Not all languages are equally supported [1]: https://venturebeat.com/2017/06/17/the-good-the-bad-and-the-ugly-of-chatbots/ steemit.com/@resteemitnow
- 6. Natural Language Processing NLG NaturalLanguage Generation Data => ‘Natural’ text Easy NLU NLG Natural Language Understanding Natural Language Generation Natural text to structured data Data to natural text Hard Easy
- 8. Types of Chatbots Easy FAQ Form Hard Complex ConversationWithin Domain Smart Home, Basic Customer Service, Basic Information Adv. Customer Service (Advanced FAQ + Form) String Distance Conversation Flow Matching Commands And Arguments Conversation Context Conversation Flow + Commands Advanced Information Database Access + Filter Matching
- 9. FAQ / SimpleInformation Lookup • User: asks questions • Chatbot: gives answers • Easy to build • No machine learning
- 10. Guided Conversation /Form • Chatbot asks questions • User answers • Slightly harder • machine learning -> extract entities
- 11. Information Lookup • Databaseaccess through conversation • User asks questions or gives commands • Chatbot gives answers, asks questions back • Difference with FAQ: Context and Entity resolution
- 12. Smart Home /Simple Information Lookup • Perform tasks • User tells Chatbot what to do • Chatbot answers/confirms/asks follow-up questions
- 13. Types of Chatbots:Personal Assistant • Collection of other chatbots • Redirects actions/questions to ‘sub’ chatbots • Seamless integration between different chatbots • e.g. Google Assistant, Siri, ...
- 14.
- 15. Common Concepts: Intents •Phrases get mapped to intents • Function calls on the chatbot →entities are arguments • Often a function in back-end
- 16. Common Concepts: Entities •Building blocks of the conversation • Entities are passed to back-end • e.g:
- 17. Common Concepts: Entities- Example • “number” entity → mapped to 3 • Request 3 of something from back-end
- 18. Common Concepts: Entities- Example • “Movies” → “requested” entity • Request movies from back-end
- 19. Common Concepts: Entities- Example • “person” entities • Passes persons as arguments
- 20. Common Concepts: Entities- Example • “with_actors” composite entity • Passes list of persons as “actors” argument
- 21. Common Concepts: Context •Context = entities from previous phrases & replies ● pagination ○ “three more please” ● changing part of the query ○ “who played in <movie>?” ○ “who directed it?” ● follow-up
- 22. Common Concepts: Prompts •Promptwhen missing required entities •e.g.: • User: “What’s the weather for today?” • Weatherbot: “For what city?”
- 23. Common Concepts: Follow-up/ Flows •Follow-up questions guide users into Flows •Example •InsureBot: “What brand of car?” •User: “<car brand>” •InsureBot: “What year was the car made?” •…
- 25. Technologies Amazon Facebook GoogleIBM Microsoft Oracle VPA Amazon Echo line - Google Home line - Cortana, Integration with Amazon Alexa Oracle Voice (ios app store) Dev Platform Alexa Voice Services, Alexa Skills Kit RESTful wit.ai backend Dialogflow IBM BlueMix Windows Oracle Mobile Cloud Services Speech Synthesis API Polly (+SSML) - unnamed (+SSML) Watson Text To Speech (Bluemix) Bing Speech API (+SSML) Nuance (paywall) NLU Lex wit.ai Dialogflow Conversation API LUIS.ai Nuance (paywall) Bot Creation Interface with Echo line Interface with Facebook Chat One-click Integration in multiple platforms BlueMix MS Bot Framework Graphical UI Free? Yes Yes Dialogflow: Yes Google cloud: Limited Yes (Limited) Yes (Limited) No Dutch? No Limited Limited Yes (Beta) Yes End 2017
- 26.
- 27. •Lower Level librariesallow you to fine-tune (e.g. in python): • spaCy • nltk • polyglot • dateparser DIY with Python
- 28. Natural Language Processing NaturalLanguage Understanding: Hard Need a lot of training data Get semantic and entities from a sentence Create structured data from unstructured natural text Natural Language Generation: Easy Create file with template sentences and insert entities Add randomness
- 29. Natural Language Understanding:Pipeline
- 31.
- 32. Confirmation •Stating the confirmationis fine for inconsequential mistakes: •“What’s the weather for Memphis for next sunday?” •“The weather for Memphis, Egypt for October 15th is …” •“Cancel. What’s the weather for Memphis, Tennessee?”
- 33. Confirmation •Asking for confirmationis better when there’s money on the line: •“Book a flight for Memphis for tomorrow.” •“Do you want me to book a flight for Memphis, Egypt on Thursday the 12th of October?” •“No, book a flight for Memphis, Tennessee.”
- 34. Confirmation •Pick between choices: •“CallEllen” •“Do you mean Ellen X or Ellen Y?” •“Ellen Y”
- 35. Buttons •for questions withclear options •with buttons: ● users are reassured they’ll be understood ● users see all possibilities ● users can do nothing wrong
- 36. Openness •Let Users Knowit’s a Bot •Bots are imperfect, users know and forgive •Bots are limited, users adapt and stay on topic •Users trust you/your bot, disguised bots break that trust
- 37. Redirect to Human •Why? •peoplewill go beyond the bot’s capabilities and require human support •When? •complicated question, not covered in FAQ •complaints •on request •How? •low traffic: contact form may suffice •high traffic: replace bot with human in same interface
- 38. Design Considerations •Design considerationsbased on goal: ● Strict, down-to-business ● Informal with slang and jokes ● Voice interface
- 39. Design Considerations: VoiceInterface Bad No buttons or images Mispronunciation Not private Pros Cons Wow-factor No buttons or images Hands-free Mispronounciations Faster than typing Not private
- 40. Testing • Write recurrenttest for your service • API’s are young and prone to change • Ensure proper working • Notify if test are broken
- 41. Conclusion: Future ● NLUimprovement ○ Speed ○ Accuracy ○ Language coverage ● More used by Businesses ● More accepted by Users
- 42.
- 43.