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Rasa Developer Summit - William Galindez Ariaz, Octesoft - Dial Rasa for Dinner


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How does a Colombian living in Prague learn a notoriously difficult language? While going beyond dry lessons and into actually understanding how to hold conversations? This session discusses how a Czech speaking Rasa bot can simulate scenarios that can spar learners to help in different situations - and different languages.

Rasa NLU allows you to explore markets with 'exotic languages' such as Czech
The bot created with Rasa is currently helping me to learn a new language and prepare myself for real case situations, such as ordering food in a foreign country
Being knowledgeable in LUIS, Watson and Rasa, I can recommend the pure ML approach of Rasa to fire up a bot (without going through tedious Dialog management)
Rasa is the technology, we dream the use cases, in my experience, my Waiter bot has a real impact in my life, what is your Idea?

William Galindez Arias is an Electronic Engineer, from Colombia, who moved to Prague to study Data Science. William works as Machine Learning Consultant for Fortune 500 companies, helping them find use cases for them to apply AI.

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Rasa Developer Summit - William Galindez Ariaz, Octesoft - Dial Rasa for Dinner

  1. 1. Dial Rasa for Dinner - Chatbot Language Learning William Galindez Arias Senior Consultant / Advisor, Octesoft Rasa Developer Summit - 2019
  2. 2. William Galindez Arias @WilliamAriasZ Dial Rasa for Dinner
  3. 3. 1. Learn Czech (a bit)
  4. 4. Process the information and reply back One Direction You learn how to say things, but hardly how to react
  5. 5. Me Real life(Test Data?)
  6. 6. 2. Problems I want to Solve
  7. 7. Learn Czech To survive Build Technical Skills Have fun
  8. 8. Learn Czech To survive What is relevant to your context ? Immediate Impact Achieve a Goal
  9. 9. Learn Czech To survive Build Technical Skills Have fun Relevant Context = Closed - Domain Vocabulary = Dataset or Corpus Achieve a goal = Goal-Oriented
  10. 10. 2.1 The making (New/ Better Problems) 2. Problems I want to Solve
  11. 11. Improved problems… starting to Have fun Word2Vec? Embeddings for Deep Learning? Rank Intents
  12. 12. Department of Linguistics Label Intent
  13. 13. PDF - CSV - Pandas DataFrame Have fun
  14. 14. Consolidated Dataset trn_data = json.dumps(czech) Rasa NLU Supervised Embeddings
  15. 15. {'intent': { 'name': 'order_food_main_dish', 'confidence': 0.8891581892967224}, 'entities': [{'start': 23, 'end': 34, 'value': 'Smažený sýr', 'entity': 'dish', 'confidence': 0.6718105031002314, 'extractor': 'ner_crf’ } ]
  16. 16. One Context: not Enough Food dialogue See a Doctor Complain …+ + Flirt?
  17. 17. Let’s give it a face: Receptionist Bot ['BotUttered (text: Hello, or should I say Ahoj?, data: {"elements": null, "quick_replies": null, "buttons": [{"payload": "call doctor program training", "title": "suggestion_1"}, {"payload": "I want to complain", "title": "suggestion_2"}, {"payload": "Food Program", "title": "suggestion_3"}]
  18. 18. Or Add Context Manually Ordering Food, Doctor, Flirt?
  19. 19. Surviving: Asking Beer Fluent Waiter, many combinations of utterances (Jabs, uppercuts, hooks) 6/10
  20. 20. Architecture v1.0 Food Doctor Complains Waiter Doctor ISP 5055 5056 Context Selector 5057 Front Rasa CustomActions DB ReceptionistBot (English) NoSQL Key Spaces Rasa Action Server Manual Selector Intent Driven Selector
  21. 21. 2.1 The making (New/ Better Problems) 2. Problems I want to Solve 2.2 Language beyond my use case
  22. 22. Robots Architecture for RPA (Automation) Front Rasa SDK CustomActions InvoicesBot RPA Cloud Orchestrator Rasa Build TechnicalSkills POST: Entities from SLOTS along with Token Cloud ERP Problem: I can’t afford Named users Licenses for ERP or Windows Proposed Solution: One Bot facing the systems (1 license) ”Hey Chatbot please process pending invoices from August 2019” Windows VMs Entities=Arguments
  23. 23. Architecture for Searching Staff HR Web NLU APIM Rasa NLU Build TechnicalSkills POST: Intent/Entities from search to API Gateway API GW µ_1 µ_2 µ_n Cassandra Cluster k1 k2 k3 Microservices Problem: Time consuming to find Staff in legacy tools Proposed Solution: Search with natural queries: ”Find me candidates with Python Skills available to start next week”
  24. 24. </end> Decision Makers of Tomorrow