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Course Outline
Advanced Natural Language Generation (NLG) Techniques Overview for Government
- Review of fundamental NLG concepts for government
- Introduction to advanced NLG methods for government
- The role of transformers in modern NLG for government
Pre-trained Models for NLG for Government
- Overview of popular pre-trained models (GPT, BERT, T5) for government applications
- Fine-tuning pre-trained models for specific tasks within the public sector
- Training custom models with large datasets for government use
Improving NLG Outputs for Government
- Ensuring coherence and relevance in text generation for government communications
- Controlling text length and content using advanced NLG methods for government documents
- Techniques for reducing repetition and enhancing fluency in government publications
Ethical and Responsible NLG for Government
- Understanding the ethical challenges of AI-generated content for government
- Addressing biases in NLG models used by government agencies
- Ensuring the responsible use of NLG technology for government operations
Hands-On with Advanced NLG Libraries for Government
- Working with Hugging Face Transformers for NLG in government applications
- Implementing GPT-3 and other state-of-the-art models for government projects
- Generating domain-specific content using NLG for government reports and communications
Evaluating NLG Systems for Government
- Techniques for evaluating NLG models in a government context
- Automated evaluation metrics (BLEU, ROUGE, METEOR) for government use
- Human evaluation methods for quality assurance in government documents
Future Trends in NLG for Government
- Emerging techniques in NLG research relevant to government operations
- Challenges and opportunities in NLG development for government agencies
- Impact of NLG on industries and content creation within the public sector
Summary and Next Steps for Government
Requirements
- Basic understanding of Natural Language Generation (NLG) concepts
- Experience with Python programming
- Familiarity with machine learning models
Audience for government
- Data scientists
- AI developers
- Machine learning engineers
14 Hours