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Course Outline
Introduction to Natural Language Generation (NLG)
- What is NLG?
- Difference between NLU and NLG
- Applications of NLG in real-world scenarios for government
Basic NLG Techniques
- Template-based generation
- Statistical models for text generation
- Introduction to machine learning in NLG for government applications
Working with NLG Models
- Overview of NLG models (GPT, T5) for government use
- Setting up basic models in Python for government projects
- Generating text using pre-trained models for government communications
Challenges in NLG
- Handling coherence and relevance in government documents
- Common issues in text generation for government reports
- Ethical considerations in AI-generated content for government
Hands-On with NLG Tools
- Introduction to NLG libraries (GPT-2/3, NLTK) for government use
- Generating text for specific use cases in government operations
- Evaluating generated text for quality in government contexts
Evaluating NLG Models
- Measuring fluency and coherence in generated text for government documents
- Automated vs. human evaluation techniques for government content
- Improving the quality of NLG outputs for government use
Future Trends in NLG
- Emerging techniques in NLG research for government applications
- Challenges and opportunities for future text generation in government
- Impact of NLG on content creation and AI development for government
Summary and Next Steps
Requirements
- Basic understanding of programming concepts
- Familiarity with Python programming
Audience
- Individuals new to artificial intelligence
- Data science professionals and enthusiasts
- Content creators interested in leveraging AI-generated text for government and other sectors
14 Hours