The document discusses conversational AI and how deep machine learning techniques can help advance natural language understanding and generation for chatbots. It describes an open-source framework for building conversational agents and its architecture, including natural language understanding, dialogue handling, and natural language generation components. The framework uses recurrent neural networks and memory networks for these tasks. It also highlights some open challenges in the field and recent papers on conversational AI research.

1. Conversational AI:
Building clever chatbots
Tom Bocklisch, Lead ML Engineer @ LASTMILE

2. Example of a live Skill:
A customer can change
her address via
Facebook Messenger
Conversational AI will
dramatically change
how your users
interact with you.

3. An open source, highly scalable ML
framework to build
conversational software

4. is a technology company developing conversational AI.
Goal: next-generation intelligent bots
Team: tight-knit, fast-moving team of researchers,
engineers, designers and product people
Location: everywhere (honestly: Berlin, Edinburgh, Beijing)
We work on the core technology for next-generation conversational AI
Founders:
Dr. Alan Nichol (CTO)
Alexander Weidauer (CEO)
Advisory Board:
Chad Fowler (MD & CTO @ Wunderlist)
Matthaus Krzykowski (former Co-Founder @ Xyo)
Cat Noone (Designer & Founder @ Iris)
Investors: Reference customers:
Introduction

5. Architectural Overview
Introduction
AI
(Natural Dialogue
Management)
(Natural Language
Understanding)
(Natural Language
Generation)
(Conversational
Platform, e.g.
Facebook
Messenger)
“What’s the weather
like tomorrow?”
(User Request via text or voice)
“It will be sunny and
20°C.”
(AI response via text or voice)
(Your backend,
database or API)

6. Under The Hood

7. Natural Language Understanding
Natural Language
Understanding
What’s the
weather like
tomorrow?
Example Entity Extraction Pipeline
”What’s the weather like tomorrow?” { “date”: “tomorrow” }
Tokenizer
Part of Speech
Tagger
Chunker
Named Entity
Recognition
Entity Extraction
Example Intent Classification Pipeline
”What’s the weather like tomorrow?” { “intent”: “request_weather” }
Vectorization Intent Classification
Under The Hood

8. Demo

9. 1. Create your training data
Demo
E.g. using the contributed rasa NLU gui at
https://golastmile.github.io/rasa-nlu-trainer/

10. 2. Configure the model
Configure the
model
Demo

11. 3. Train
Training the
model
Demo

12. 4. Use Model
Playing around
with the trained
model
Demo

13. Under The Hood

14. Dialogue Handling
Under The Hood
“What’s the weather
like tomorrow?”
Intent
Entities
next
ActionState
previous
Action
“Thanks.”
after next
Action
updated
State
“It will be sunny
and 20°C.”
SVM
Recurrent NN
...

15. Detailed Dialogue Handling
Under The Hood
“What’s the weather
like tomorrow?”
“It will be sunny and
20°C.”
Entity
Input
Action Mask
Renormal-
ization
Sample
action
Action
type?
Response
API Call
Recurrent
NN
API Call
Entity
Output
Intent
Classification
Entity
Extraction
Similar to LSTM-dialogue prediction paper: https://arxiv.org/abs/1606.01269

16. Final Thoughts

17. Open Challenges
Final Thoughts
Challenges for curious minds:
●
● Unsupervised multi-language entity recognition
● Dialogue generalisation (e.g. optional questions)
Chit-Chat
model
Task-Oriented
model
I want to travel
to Spain.
?
● Combination of different dialogue models

18. Current Research
Final Thoughts
Good reads for a rainy day:
● Last Words: Computational Linguistics and Deep Learning (blog)
https://goo.gl/lGSRuj
● Memory Networks (paper)
https://arxiv.org/pdf/1410.3916
● End-to-End dialogue system using RNN (paper)
https://arxiv.org/pdf/1604.04562.pdf
● MemN2N in python (github)
https://github.com/vinhkhuc/MemN2N-babi-python

19. ● Conversational AI is a big part of the future
● Deep ML techniques help advance state of the art NLU
and conversational AI
● Open source is strategically important for enterprises
implementing AI
Summary
Final Thoughts
3 take home thoughts:

20. Get in
touch!
Tom Bocklisch
Lead ML Engineer
tom@golastmile.com