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Course Outline
Introduction to AI-Driven NLG
- Overview of Natural Language Generation (NLG)
- Role of NLG in conversational AI systems
- Key differences between NLU and NLG
Deep Learning Techniques for NLG
- Transformers and pre-trained language models
- Training models for dialogue generation
- Handling long-term dependencies in conversation
Chatbot Frameworks and NLG
- Integrating NLG with chatbot platforms (e.g., Rasa, BotPress)
- Generating personalized responses for chatbots
- Improving user engagement through contextual AI
Advanced NLG Models for Virtual Assistants
- Using GPT-3, BERT, and other cutting-edge models
- Generating multi-turn dialogues with AI
- Improving fluency and naturalness in virtual assistant responses
Ethical and Practical Considerations
- Bias in AI-generated content and how to mitigate it
- Ensuring transparency and trustworthiness in chatbot interactions
- Privacy and security considerations for virtual assistants
Evaluation and Optimization of NLG Systems
- Evaluating NLG quality: BLEU, ROUGE, and human evaluation
- Tuning and optimizing NLG performance for real-time applications
- Adapting NLG for domain-specific use cases
Future Trends in NLG and Conversational AI
- Emerging techniques in self-supervised learning for NLG
- Leveraging multimodal AI for more interactive conversations
- Advances in context-aware conversational AI
Summary and Next Steps
Requirements
- Strong understanding of Natural Language Processing (NLP) concepts
- Experience with machine learning and AI models
- Familiarity with Python programming
Audience
- AI developers
- Chatbot designers
- Virtual assistant engineers
21 Hours
