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Rasa AI: Building clever chatbots

Rasa AI: Building clever chatbots

Slides from a talk about rasa AI at the wearedevelopers conference vienna in may 2017

Tom Bocklisch

May 10, 2017

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  1. 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.
  2. 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
  3. 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)
  4. 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
  5. 1. Create your training data Demo E.g. using the contributed

    rasa NLU gui at https://golastmile.github.io/rasa-nlu-trainer/
  6. Dialogue Handling Under The Hood “What’s the weather like tomorrow?”

    Intent Entities next Action State previous Action “Thanks.” after next Action updated State “It will be sunny and 20°C.” SVM Recurrent NN ...
  7. 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
  8. 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
  9. 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
  10. • 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: