A novel chatbot architecture to protect your privacy Chatbots are getting more and more public attention, often replacing repetitive customer support tasks. Some common frameworks that enable us to “understand” natural language are proprietary and a careless implementation might compromise the privacy of the user and host. We propose a novel type of middleware that allows an easy exchange between natural language understanding (NLU) and messaging platforms, allowing the host to easily switch between multiple platforms. This middleware provides a translation layer between the components that is easily extendable with your own adapters. It also allows the developer to intercept messages at every point so that you can decide what happens with your or the user’s data. Additionally, the information passed to (possibly proprietary) messaging and NLU services is reduced, allowing you to pass the minimum amount of information to third party services which is required to fulfill their respective tasks.

1. HOW TO TAME YOUR CHATBOT
A novel chatbot architecture to protect your privacy
Fiete Lüer
15. November 2019

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TABLE OF CONTENTS
1. INTRODUCTION
2. WHAT’S WRONG?
3. HOW COULD IT BE CHANGED?
4. IMPLEMENTATION: EMUBOT
5. CONCLUSION
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INTRODUCTION
NLP / NLU
NLP
Natural Language Processing sentiment analysis
summarization
relation extraction
machine translation
automated speech
recognition
question answering
named entity recognition
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INTRODUCTION
NLP / NLU
NLP
Natural Language Processing
NLU
Natural Language Understanding
sentiment analysis
summarization
relation extraction
machine translation
automated speech
recognition
question answering
named entity recognition
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INTRODUCTION
Standard Procedure - Training
1. Define what you want your bot to answer
→ Which intents should be handled?
2. Define terms/groupings of words that are relevant to understand the intent
→ Which entities are important?
3. Define (often static) responses
4. Implement advanced concepts
→ e.g. contexts, actions for non-static content
NLP-SERVICE
An NLP service provider:
Dialogflow, LUIS, Lex, Rasa, Snips, ...
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INTRODUCTION
Standard Procedure - Inference
NLP-SERVICE
An NLP service provider:
Dialogflow, LUIS, Lex, Rasa, Snips, ...
MESSAGING PLATFORM
A messaging service:
Facebook Messenger, Slack,
Website, Google Chat, ...
Text query
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INTRODUCTION
Standard Procedure - Inference
NLP-SERVICE
An NLP service provider:
Dialogflow, LUIS, Lex, Rasa, Snips, ...
MESSAGING PLATFORM
A messaging service:
Facebook Messenger, Slack,
Website, Google Chat, ...
Text query
Determine the intention of the user:
What does the user want?
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INTRODUCTION
Standard Procedure - Inference
NLP-SERVICE
An NLP service provider:
Dialogflow, LUIS, Lex, Rasa, Snips, ...
MESSAGING PLATFORM
A messaging service:
Facebook Messenger, Slack,
Website, Google Chat, ...
Text query
Determine the intention of the user:
What does the user want?
Response: static (often from NLP-Service)
or dynamic (some predefined action)
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WHAT’S WRONG?
(Some) Weaknesses
● Non-tech / inexperienced: Out-of-the-box solutions!
● One-click integrations!
■ Give me your keys and we will handle everything. Your user data is safe™!
● Frameworks: minimal examples
■ Keep your keys.
■ Keep the functionality and complexity minimal
■ Often quite outdated, too simplistic, and barely documented
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HOW COULD IT BE CHANGED?
Proposed Architecture - Key Ideas
● Logically separated entities
● Easy to include means to protect the privacy of host and user
● Expandability by design
● Easy setup, minimize the required configuration
● Minimize overhead
● Thorough documentation
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MESSAGING PLATFORM NLP-SERVICE
FACEBOOK MESSENGER
SLACK
ADDITIONAL SERVICES
VIA CUSTOM ADAPTER
ADDITIONAL SERVICES
VIA CUSTOM ADAPTER
SNIPS
RASA.AI
DIALOGFLOW
WEBSITE
YOUR OWN
NLP-SERVICE
DIFFERENT NLP
SERVICE (AMAZON LEX,
FACEBOOK WIT.AI …)
DIFFERENT MESSENGER
(GOOGLE CHAT,
ROCKET CHAT …)
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MESSAGING PLATFORM NLP-SERVICE
FACEBOOK MESSENGER
SLACK
ADDITIONAL SERVICES
VIA CUSTOM ADAPTER
ADDITIONAL SERVICES
VIA CUSTOM ADAPTER
SNIPS
RASA.AI
DIALOGFLOW
WEBSITE
YOUR OWN
NLP-SERVICE
DIFFERENT NLP
SERVICE (AMAZON LEX,
FACEBOOK WIT.AI …)
DIFFERENT MESSENGER
(GOOGLE CHAT,
ROCKET CHAT …)
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SERVER
EMUBOT
COREADAPTER
LAYER CHAT
ADAPTER
LAYER NLP
NLP
ADAPTER
CHAT
ADAPTER
MESSAGE
HANDLER

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MESSAGING PLATFORM NLP-SERVICE
FACEBOOK MESSENGER
SLACK
ADDITIONAL SERVICES
VIA CUSTOM ADAPTER
ADDITIONAL SERVICES
VIA CUSTOM ADAPTER
SNIPS
RASA.AI
DIALOGFLOW
WEBSITE
YOUR OWN
NLP-SERVICE
DIFFERENT NLP
SERVICE (AMAZON LEX,
FACEBOOK WIT.AI …)
DIFFERENT MESSENGER
(GOOGLE CHAT,
ROCKET CHAT …)
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SERVER
EMUBOT
COREADAPTER
LAYER CHAT
ADAPTER
LAYER NLP
CUSTOM CODE
INTERFACE
CHAT TO CORE
INTERFACE
NLP TO CORE
INTERFACE
NLP TO NLP
ADDITIONAL
SERVICES, E.G.
EXTERNAL APIs
DATABASE
MESSAGE
MANIPULATION
NLP
ADAPTER
CHAT
ADAPTER
MESSAGE
HANDLER

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IMPLEMENTATION
emubot
● Different setups:
● Basic framework: (currently) similar to one-click integration, mainly to offer the functionality, typings, ...
● Minimal example: via npm, mirror messages at interceptors, similar to one-click integration
● Extensive example: pseudonymize the user id, execute actions, connect to database, …
● Typescript
● (Soon to be) open source at github.com/emundo/emubot
● Extensive documentation including
● the basic setup,
● information on adapters, and
● advanced topics (like containerization)
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CONCLUSION
● Current connectors/frameworks are usually solely focusing on offering minimal examples
● emubot:
● Currently no pseudonymization by default, but included in one of the extended examples
● Adapters for several platforms, filtering most relevant data from queries
■ Simple extension using your own adapters for personal use cases
● Easy setup for beginners: only need to modify the config file, compile and start the bot
● Arbitrarily complex extensions for advanced users: Logical separation of components
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16. Fiete Lüer
fiete.lueer@e-mundo.de
+49 (0) 89 665 98 45 - 06
Maxim Dolgich
maxim.dolgich@e-mundo.de
Do not hesitate to contact us!