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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