1.
Tutorial on Chatbot Development
with Xatkit
Easiest way to create advanced (chat)bots and digital assistants - #OSS #low-code
@xatkit – xatkit.com

2.
Why Chatbots

3.
“Business Insider experts predict that by 2020, 80% of enterprises will use chatbots. … by 2022, banks can
automate up to 90% of their customer interaction using chatbots … 40% of large companies employing more
than 500 people plan to implement one or more intelligent assistant…” - https://chatbotsmagazine.com/chatbot-report-
2019-global-trends-and-analysis-a487afec05b
Chatbot market to surpass 1B USD by 2021

4.
But what is exactly a Chatbot?
• Chatbot = Bot + Conversational Interface
CI = Chat interface OR voice interface
• Chatbot = Digital Assistant

5.
Challenges in building Chatbots

6.
Let’s create a chatbot to help
newcomers to write issues on Github!
Alright! It’s just a set of questions &
answers, this will be pretty simple!
Narrator It wasn’t.
Once upon a time
(when we were naïve enough to think that creating a chatbot would be easy)

7.
Chatbot applications
Our idea of the kind of rules we were hoping to write
User Intent
Action Parameters
If the User Wants To Open Issue
Reply « In which repository? » on Slack
Platform

8.
What went wrong? – Many pieces to put
together
Input/Output
Messaging channels
External
Platforms
NLU Engine

9.
Chatbots are complex systems
Conversation Logic
Text Processing
External Services
Messaging
Platforms
Deployment
Evolution
Maintenance
Tests

10.
At the core: NLP/NLU component
• NLP: Natural Language Processing. NLU: Natural Language
Understanding. In theory, NLU is a more sophisticated version of
NLP. In practice they are both used mostly indistinctintly
• Two key missions
• Classify the user utterance in one of the intents defined in the
bot
• Identify the parameters (called « entities ») from the
utterance

11.
NLU approaches
• Regular expressions
• Require an almost exact match. Not used
• Grammar-based approaches
• Useful for small domains with a very specific vocabulary and constructs
• E.g. Stanford Parser
• Neural Networks - Multiclass classifiers
• You provide some training sentences for each intent (the "classes" ). The
classifier learns from these sentences and tries to assign any new input text to
one of the classes.
• It’s most common approach nowadays

12.
Common tricks in NLUs
• Tokenizers split the sentences into words
• Stemmers identify the lemma of the word (e.g. to focus on the verb, not on
the tense of the verb for matching purposes)
• Synonyms are an automatic way to expand the training sentences
• Entity augmentation also generalize training sentences
• E.g. What’s the weather in Barcelona -> What’s the weather in @city (the intent will
match no matter what city the user asks for, no need to train the classifier with all
the cities of the world as it will already know the list of cities itself).
• Augmentation is possible for a large number of data types and common concepts
(measures, countries, dates,…)
https://medium.com/@jseijas/how-to-build-your-own-nlp-for-chatbots-9b4c08eb03a9

13.
Chatbot Development
Platforms

14.
The AI
ecosystem
is huge

15.
>100 chatbot platforms
• Rasa, BotPress, Chatfuel, Inbenta, Botsify,
Flow XO,…
• And quite a few NLU providers: DialogFlow,
Lex, LUIS, Watson, NLP.js

16.
… but
• Only around 15% are open source
• Not flexible (i.e. difficult to integrate other platforms)
• Not interoperable
• Low-level (as soon as you need the bot to do some
action you end up programming in JS)

17.
• Grady Booch – history of softwre engineering
The entire history of software engineering is that of
the rise in levels of abstraction
- Grady Booch
Also true for AI/chatbots ->
Chatbot Modeling to the rescue!

18.
The Xatkit solution

19.
Xatkit – Key Concepts

20.
Xatkit Framework
• Raise the level of abstraction at what chatbots are defined
• Focus on the core logic of the chatbot
• Conversation and user interactions
• Action computations
• Independent from specific implementation technologies
• Automatize the deployment and execution of the modeled chatbot
• Deploy over multiple platforms
• Execute the chatbot logic

21.
Xatkit is:
• A model-based & low-code chatbot development platform
• Where chatbots are defined using a couple of external DSLs
• And with a runtime engine that deploys and executes the bots over
the desired platforms

22.
In Xatkit, platforms are
• An abstraction of any input/output messaging tool and any external
service. Platform interfaces are also defined with a DSL
• Platforms offer actions (that the bot can execute) and events (whom the
bot can subscribe to)
• PIM/PSM : Bots can be defined using generic platforms that are then
concretely instantiated during the deployment. No need to change the bot
• Any new platform becomes immediately available to any existing bot

23.
Voice support
• Voice interfaces (like Alexa) are just another platform
• Voice is translated into text (speech2text tools) and then processed as
if the user had written it.

24.
Xatkit Architecture

25.
Xatkit Framework
Chatbot
Designer
Intent Recognition NLU Providers
(platform-specific)
Platform Package
Intent Package
Xatkit Modeling Language
Chatbot
User
Instant
Messaging
Platforms
Xatkit Runtime
Execution Package
uses
uses
Platform-independent
chatbot definition
External
Services
Deployment
Configuration
Platform
Designer

26.
Intent Language (example bot to create new issues in a GH repo)
intent OpenBug {
inputs {
"The plugin is not working"
"I have a problem with the plugin"
"I'd like to report an error"
"I want to open a bug"
"There is an error in the plugin"
}
}
intent DescribeBug {
inputs { "My error is Error"
"The problem is Error"
"I get this error: Error"
"I get the error Error“ }
creates context bug {
sets parameter title from fragment Error (entity any)
}
}
• Inputs defines the set of training sentences for the intent
• A context records variables to be used later on in the execution model. Here we
store the info about the error in the title attribute of the Repository context.
• Entity defines the type of the value to be matched (any is anything, number would match
numeric values,…). You can also define your own enumeration (e.g. cities) to be even
more precise

27.
Execution language
• The execution model follows state machine-like semantics
• Each state in the execution language contains 3 sections
• Body (optional): the bot reaction, executed when entering the state.
• Next (mandatory): the outgoing transitions defined as condition –> State. The
conditions are evaluated when an event/intent is received. If a transition is
navigable the execution engine moves to the specified state. Transition
conditions can be as complex as you want.
• Fallback (optional): Arbitrary code (as the Body section) that will be executed
if the engine cannot find a navigable transition.

28.
Execution language
• An execution model also contains 2 special states:
• Init: a regular state that is entered when the user session is created. It can
contain a Body, Next, and Fallback section.
• Default_Fallback: This state represents the default fallback code to execute
when there is no local fallback defined in a state. It can be used to print a
generic error message (“Sorry I didn’t get it“), while local fallback can print a
custom message tailored to the current state (“Please reply by yes or no“).
• States can define a single wildcard transition (using the reserved
character _ as the condition) that will be automatically navigated
when the state’s body has been computed.

29.
Execution Language Example
import platform "GithubPlatform"
import platform "SlackPlatform"
use provider SlackPlatform.SlackIntentProvider
use provider GithubPlatform.GithubWebhookEventProvider
Init {
Next {
event == Issue_Opened --> HandleIssueOpened
intent == GetIssue --> HandleGetIssue
intent == OpenBug --> HandleOpenBug
}
}
• The import platform allows you to easily interact with any existing platform, e.g.
replying on Slack or asking GH to create the issue
• The init state checks whether the user wants to get information about an issue, open a
new bug, or it is GitHub itself that sends us an event to notify of changes in the repo

30.
Execution Language Example
HandleOpenBug {
Body { SlackPlatform.Reply("Can you please describe what is wrong in a few words?")}
Next { intent == DescribeBug --> HandleDescribeBug }
}
Default_Fallback {
Body { SlackPlatform.Reply("Sorry, I didn't get it") }
}
• If the user wants to open the new bug we start the process of collecting the required
data
• If we don’t manage to understand what the users says, the default fallback will be
executed instead.

31.
Platform definition
• The execution model can directly refer to all predefined platforms in
Xatkit. 10+ right now: Slack, Zapier, Alexa, UML, Giphy, GitHub,
Neo4j,…
• Chatbot designers only need to define a new platform if they have
special needs. This implies:
• Declaring the platform interface
• Implementing the platform itself as a Java class in the proper place in the
platform hierarchy
• Plenty of auxiliary classes and boilerplate code facilitate this implementation

32.
Platform Language
Abstract platform Chat
actions {
PostMessage(message, channel)
Reply(message)
}
}
Platform Slack extends Chat
Platform Discord extends Chat
Platform Github
actions {
OpenIssue(repository, title, content)
GetIssue(user, repository, issueNumber)
…
}
event Issue_Opened
event Issue_Edited
…
• Platforms are also declaratively defined
• Platform hierarchy facilitates reusability and genericity
• Platforms offer actions to be executed and events to subscribe to

33.
The compute
method
shows how
we
implemented
the
PostMessage
action in
Slack

34.
Xatkit Framework Intent Recognition Providers
(platform-specific)
Platform Package
Intent Package
Xatkit Modeling Language
Instant
Messaging
Platforms
Xatkit Runtime
Execution Package
uses
uses
Platform-independent
chatbot definition
External
Services
Deployment
Configuration
Chatbot
Designer Chatbot
User
Platform
Designer

35.
Runtime Component
• Generic event-based execution engine
• Loads the platform-specific connectors referenced in the bot definition
• Automatic deployment
• Execution life-cycle
• Inputs
• Chatbot model (defined with the Xatkit modeling language)
• Configuration file (Oauth tokens, API keys, platform-specific parameters,…)

36.
Runtime configuration example
// Intent Recognition Provider Configuration
xatkit.intent.recognition = DialogFlow
xatkit.dialogflow.project = dialogflow_project_id
xatkit.dialogflow.credentials = key.json
// Platform configuration
xatkit.slack.credentials = slack_oauth_token
xatkit.discord.credentials = discord_credentials
xatkit.github.credentials = github_oauth_token

37.
Xatkit Tool

38.
Tool Support
• Available on GitHub as an independent organization:
https://github.com/xatkit-bot-platform/ to facilitate external
contributions
• Open source (EPL v2)
• With tutorials and examples to help you get started building bots!

39.
It comes with an Eclipse Editor – Xtext-based

40.
Care to contribute?
• With your own examples
• Improving our platform support
• Extensions to the current platforms with new actions or events
• New platforms
• Our own NLP connectors
• Adding support for new providers
• Or in any of the many other ways you can help an OSS project
https://livablesoftware.com/5-ways-to-thank-open-source-
maintainers/