Build Intelligent Conversational Agents with Amazon Neptune and Amazon SageMaker (AMT316) - AWS re:Invent 2018
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Building automated conversational agents is a balancing act between fine-grained control of messaging and maintaining logic. In this chalk talk, CarLabs, a leader in developing digital assistants for automotive brands, describes how they create their platform using a combination of Amazon Neptune to encode business rules, Amazon SageMaker to create a RNN model, and Amazon Mechanical Turk to determine the highest levels of accuracy.
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Build Intelligent Conversational Agents with Amazon Neptune and Amazon SageMaker (AMT316) - AWS re:Invent 2018
- 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Build Intelligent Conversational Agents with Amazon Neptune and Amazon SageMaker Sam Havens CTO CarLabs.ai A M T 3 1 6 Keji Xu Solutions Architect AWS
- 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda Motivation, Demo, Architecture Amazon Neptune: Slot Filling and More Amazon SageMaker: Machine Learning Instead of Retrieval Amazon Mechanical Turk: Does This Look Right To You? Discussion
- 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Motivation and demo
- 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Motivation • Car manufacturers are not used to the increasing customer service demands they face • Dealers handled customer communication, but that is changing • We build scalable, one-on-one communication software • Why not just use Amazon Lex? • Want to be able to fine tune language model to our domain (automotive) • Separation of content creation (CMS) and functionality (framework - compare vehicles programmatically, etc.) • Scope creep is desirable • We want to handle as much for our clients as possible, so scope creep is good! • However, the tooling we build and use needs to be useful for unforeseen use cases • GraphDB (Neptune), Machine Learning (Amazon SageMaker), and human validation (MTurk) are all very flexible
- 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Architecture
- 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Architecture • User inputs text/audio/image • Send to Amazon Lex/LUIS/DialogFlow/custom model to classify as In-Domain or Out Of Domain (ID or OOD) • If ID, get intent, entities from same service • (ID) Based on intent/entities, get template from CMS • Want tight control over ID content, so use templates • User requests group of cars + hybrid => return template “Explore_Alt_Fuel” • (OOD) Get response directly from seq2seq model • seq2seq model is an RNN architecture used in translation • Also common in question answering - “translate” question to answer
- 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Neptune CMS Architecture
- 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Neptune: Slot filling and more
- 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Retrieval and more with a graph DB • Built a CMS (content management system) • Given an intent, entities, and user state, retrieves and fills in templates • Good, but couldn’t control flow (slot filling and API fulfillment) • Chose graph DB for business rule implementation • Flexibility is the main factor in choosing a graph • Allows content editors to control content, flow, and data fetching w/o code • Except the GraphQL queries • Nodes and edges • We use ‘hasParameter’ and ‘hasAction’ as our edges • Nodes can have arbitrary attributes • GraphQL templated queries to retrieve needed data • Validation rules
- 11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Business rules in the graph
- 12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Business rules in the graph
- 13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Business rules in the graph
- 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker NLP architecture
- 15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker: Retrieve Translate
- 16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker • We use Amazon SageMaker to productionalize notebooks • It can do more • Push button training and deploy • Hosted endpoint • Provides an HTTPS endpoint
- 17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Generate, don’t just retrieve, with ML • OOD content is infinite and not worth managing • Hence retrieval methods are not appropriate • “Hey” => “Hi” - don’t want to store things like this • CakeChat: Emotional Generative Dialog System (one OSS option) • Uses end-to-end trained embeddings of 5 different emotions to generate responses conditioned by a given emotion • Already Dockerized, so easy to deploy to Amazon SageMaker! • Gives pretty good responses to the “long tail” questions • “so sad” => “What happened?” • “where should we go?” => “I’m in the city”
- 18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. MTurk: Does this look right to you?
- 19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Send user input / bot response pairs to MTurk • Format logs and upload subset to MTurk • Each HIT is a question/answer pair • Turker is asked if the response is appropriate • We ask 2 Turkers • For now, “correct” == “at least one Turker approves”
- 20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Discussion
- 21. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Sam Havens @sam_havens sam@carlabs.ai
- 22. Please complete the session survey in the mobile app. ! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Appendix
- 24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Model Architecture