This document discusses using a chatbot to learn the Czech language and solve various problems. It proposes: 1. Creating a receptionist chatbot that can handle ordering food, seeing a doctor, or complaining, to learn Czech in a realistic conversational context. 2. Improving the problems the chatbot aims to solve by adding more contexts like flirting, and consolidating related datasets to build the chatbot's knowledge. 3. Exploring using the chatbot architecture for other tasks like automating invoice processing or searching a staff database, to build technical skills.

1. Dial Rasa for Dinner - Chatbot Language Learning
William Galindez Arias
Senior Consultant / Advisor, Octesoft
Rasa Developer Summit - 2019

2. William Galindez Arias
@WilliamAriasZ
Dial Rasa for Dinner

3. 1. Learn Czech (a bit)

5. Process the information and reply back
One Direction
You learn how to say things, but hardly
how to react

6. Me
Real life(Test Data?)

7. 2. Problems I want to Solve

8. Learn Czech
To survive
Build
Technical Skills
Have fun

9. Learn Czech
To survive
What is relevant to your
context ?
Immediate Impact
Achieve a Goal

10. Learn Czech
To survive
Build
Technical Skills
Have fun
Relevant Context = Closed - Domain
Vocabulary = Dataset or Corpus
Achieve a goal = Goal-Oriented

11. 2.1 The making (New/ Better Problems)
2. Problems I want to Solve

12. Improved problems… starting to Have fun
https://spacy.io/usage/models
Word2Vec?
Embeddings for Deep Learning? Rank Intents

13. https://github.com/UFAL-DSG/cs_restaurant_dataset
Department of Linguistics
Label
Intent

14. PDF - CSV - Pandas DataFrame Have fun

15. Consolidated
Dataset
trn_data = json.dumps(czech) Rasa NLU
Supervised Embeddings

16. {'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’
}
]

17. One Context: not Enough
Food dialogue See a Doctor Complain
…+ + Flirt?

18. 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"}]

19. Or Add Context Manually
Ordering Food, Doctor, Flirt?

20. Surviving:
Asking Beer
Fluent Waiter, many combinations
of utterances (Jabs, uppercuts,
hooks)
6/10

22. 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

23. 2.1 The making (New/ Better Problems)
2. Problems I want to Solve
2.2 Language beyond my use case

24. 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

25. 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”

26. </end>
Decision Makers of Tomorrow