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Course Outline
Introduction to AI-Driven NLG
- Overview of Natural Language Generation (NLG).
- The role of NLG in conversational AI systems.
- Key distinctions between NLU and NLG.
Deep Learning Techniques for NLG
- Transformers and pre-trained language models.
- Training models specifically for dialogue generation.
- Managing long-term dependencies in conversations.
Chatbot Frameworks and NLG
- Integrating NLG with chatbot platforms (e.g., Rasa, BotPress).
- Generating personalized responses for chatbots.
- Enhancing user engagement through contextual AI.
Advanced NLG Models for Virtual Assistants
- Utilizing GPT-3, BERT, and other state-of-the-art models.
- Generating multi-turn dialogues with AI.
- Improving fluency and naturalness in virtual assistant responses.
Ethical and Practical Considerations
- Addressing bias in AI-generated content and mitigation strategies.
- Ensuring transparency and trustworthiness in chatbot interactions.
- Privacy and security considerations for virtual assistants.
Evaluation and Optimization of NLG Systems
- Evaluating NLG quality using metrics like 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
- A solid understanding of Natural Language Processing (NLP) concepts.
- Experience with machine learning and AI models.
- Familiarity with Python programming.
Target Audience
- AI developers.
- Chatbot designers.
- Virtual assistant engineers.
21 Hours
