Course Outline

Introduction to Natural Language Generation (NLG) for Government

  • What is NLG?
  • Difference between NLU and NLG
  • Applications of NLG in real-world scenarios for government

Basic NLG Techniques for Government

  • Template-based generation
  • Statistical models for text generation
  • Introduction to machine learning in NLG for government applications

Working with NLG Models for Government

  • Overview of NLG models (GPT, T5)
  • Setting up basic models in Python for government use
  • Generating text using pre-trained models for government tasks

Challenges in NLG for Government

  • Handling coherence and relevance in government communications
  • Common issues in text generation for government documents
  • Ethical considerations in AI-generated content for government

Hands-On with NLG Tools for Government

  • Introduction to NLG libraries (GPT-2/3, NLTK) for government use
  • Generating text for specific government use cases
  • Evaluating generated text for quality in a government context

Evaluating NLG Models for Government

  • Measuring fluency and coherence in generated text for government documents
  • Automated vs. human evaluation techniques for government applications
  • Improving the quality of NLG outputs for government use

Future Trends in NLG for Government

  • Emerging techniques in NLG research for government
  • Challenges and opportunities for future text generation in government
  • Impact of NLG on content creation and AI development for government agencies

Summary and Next Steps for Government

Requirements

  • Basic understanding of programming concepts
  • Familiarity with Python programming

Audience

  • Beginners in artificial intelligence
  • Data science professionals
  • Content creators interested in AI-generated text for government applications
 14 Hours

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