Rasa Developer Summit - Mady Mantha, Sirius Computer Solutions - Making the best NLU with Rasa and Bert
Bidirectional Encoder Representations from Transformers (BERT) is a NLP pre-training technique released by Google. BERT's key innovation is its ability to pre-train bidirectional, contextual language representations modeled on a large text corpus. The model can then be used for downstream NLP tasks like Natural Language Understanding (NLU) and question answering. Named Entity Recognition (NER) is a subtask of NLU that attempts to identify and classify entities in a given text into pre-defined categories like names, places, organizations, currency, and quantities. A NER model can be trained using BERT. Integration of BERT NER with Rasa using a custom pipeline resulted in highly performant NLP and engaging conversations between humans and Rasa agents. WHAT YOU'LL LEARN Custom NLU pipeline with BERT integration How to build highly performant NLP Mady Mantha is a machine learning architect and AI platform leader with nearly 9 years of experience in the technology industry focused on conversational AI, natural language processing, and deep learning. She graduated from Georgetown University and is a space enthusiast.
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Rasa Developer Summit - Mady Mantha, Sirius Computer Solutions - Making the best NLU with Rasa and Bert
- 1. Making the best NLU with Rasa and BERT Mady Mantha AI Platform Leader, Sirius Computer Solutions Rasa Developer Summit - 2019
- 2. Handling Entity Disambiguation with BERT Mady Mantha AI Platform Leader, Sirius Computer Solutions
- 3. AI that works for you
- 4. What we’ll talk about • NLP overview • Problem space • Rasa-BERT integration • How does BERT fare? • Next Steps and Lessons Learned
- 8. 7www.siriuscom.com Body Slide Naturally conversational Thoughtfully contextual Learns and Evolves Mobile First to AI First
- 9. At the heart of this: NLP
- 10. Proactive, contextual assistance Intelligent conversationsQ&A Naturally conversational, contextual, intelligent assistants Surfacing the right information, at the right time, in the right channel
- 11. Bag of Words One Hot Encoding Conditional Random Fields (CRFs) Long Short Term Memory (LSTMs) Attention using Transformers Embed Encode Predict Bi-Directional Pre-training The NLP Revolution ELMo, GPT, BERT, XLNet, RoBERTa
- 12. 11www.siriuscom.com Body Slide pre-train bidirectional, contextual language representations modeled on a large text corpus
- 13. Bidirectional Encoder Representations from Transformers (BERT) • Ability to pre-train bidirectional, contextual language representations modeled on a large text corpus. • As a result, the pre-trained BERT model can be fine- tuned with just one additional output layer to create SOTA models for a wide range of tasks
- 17. Highly performant, custom, contextual NLP
- 18. Coming in clutch with NLG
- 19. Continuous Improvement Pipeline Automate data aggregation through APIs connected to Data on Demand, MyTech, SN, etc. APIs management of platform Templatized packaging of services for deployment across platforms. Democratized pragmatic AI Tracking users conversations and history to provide more personalized recommendations. Building personalized database Integrate SCONE with chat framework of choice to stand up conversational AI Chat framework integration Leverage GPT-2 and other language models to use NLG for smalltalk Natural Language Generation Customized, contextualized, and complementary experience driven by continuous improvement Quick front end deployment Collect industry vernacular to construct dictionary Custom Domain Knowledge base Build prototype of conversation for UX approval and user testing Conversation Prototypes