AI-Driven NLG for Chatbots and Virtual Assistants

• 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

This course covers the advanced techniques in Natural Language Generation (NLG) for developing AI-powered chatbots and virtual assistants. Participants will explore the latest NLG models and their applications in creating more dynamic, responsive, and human-like conversation systems.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to master AI-driven NLG techniques for chatbot and virtual assistant applications.

By the end of this training, participants will be able to:

• Understand the core principles of NLG for conversational AI.
• Leverage deep learning techniques to enhance chatbot dialogue generation.
• Integrate state-of-the-art NLG models like GPT-3 into chatbot frameworks.
• Apply contextual AI for improving conversational coherence and fluidity.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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