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Course Outline
Foundations of AI-Driven NLG
- Introduction to Natural Language Generation (NLG)
- The function of NLG in conversational AI frameworks
- Distinguishing between NLU and NLG
Deep Learning Approaches for NLG
- Transformers and pre-trained language models
- Training architectures for dialogue production
- Managing long-term dependencies in dialogue
NLG in Chatbot Frameworks
- Integrating NLG with platforms like Rasa and BotPress
- Creating tailored responses for chatbots
- Boosting user engagement via contextual AI
Advanced NLG Models for Virtual Assistants
- Utilizing cutting-edge models such as GPT-3 and BERT
- Producing multi-turn dialogues with AI
- Enhancing the fluency and naturalness of assistant responses
Ethical and Practical Implications
- Addressing and mitigating bias in AI-generated content
- Maintaining transparency and trust in chatbot interactions
- Privacy and security factors for virtual assistants
Assessing and Refining NLG Systems
- Measuring NLG quality using BLEU, ROUGE, and human assessment
- Optimizing NLG performance for real-time scenarios
- Tailoring NLG for specific industry applications
Emerging Trends in NLG and Conversational AI
- New approaches in self-supervised learning for NLG
- Utilizing multimodal AI for richer conversations
- Progress in context-sensitive conversational AI
Recap and Future Directions
Requirements
- Profound knowledge of Natural Language Processing (NLP) principles
- Practical experience with machine learning and AI architectures
- Competence in Python programming
Target Audience
- AI Engineers
- Chatbot Architects
- Virtual Assistant Specialists
21 Hours
