Covers: implementation of Intent Classification
Estimated time needed to finish: 20 minutes
Questions this item addresses:
  • How do you implement Intent Classification with a State of the art Model?
How to use this item?

Go through the article and code along if possible.

Author(s) / creator(s) / reference(s)
Venelin Valkov - KDNuggets
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Building Your Chatbot With Rasa-nlu

Contributors
Total time needed: ~2 hours
Objectives
The user of the short list will be able to understand the role of the Natural Language Understanding component of a task-oriented chatbot and build it for their own use case with conceptual knowledge and RASA hands-on guide.
Potential Use Cases
You are looking to build a chatbot for yourself or your company or are learning the fundamentals about how chatbots work.
Who is This For ?
INTERMEDIATEPython Developers new to Chatbots
Click on each of the following annotated items to see details.
Resources5/8
ARTICLE 1. An Introduction to Chatbot NLU
  • A short introduction on chatbots, NLU and NLU frameworks in the market.
10 minutes
PAPER 2. Recent Challenges in Task-Oriented Dialogue System
  • What are the state of the art model for chatbot NLU in academia?
10 minutes
ARTICLE 3. A short refresher on text classification
  • How do supervised text classification work (tokenization, train-test, evaluation)?
15 minutes
ARTICLE 4. Implementing Intent Classification with LSTM model
  • How do you implement Intent Classification?
15 minutes
ARTICLE 5. Implement Intent Classification with State of the Art BERT model
  • How do you implement Intent Classification with a State of the art Model?
20 minutes
ARTICLE 6. Implement Slot Filling with Recurrent Neural Network
  • How does Slot Filling work? What are the input and labels and how to train them using deep learning?
20 minutes
ARTICLE 7. A short tutorial on how to use RASA NLU and RASA core
  • How to implement a simple POC chatbot with RASA?
20 minutes
WRITEUP 8. Other useful links
  • What if I want to learn more about Chatbots and RASA?
20 minutes

Concepts Covered

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