Advanced Techniques in Natural Language Generation (NLG)

• Basic understanding of NLG concepts
• Experience with Python programming
• Familiarity with machine learning models

Audience

• Data scientists
• AI developers
• Machine learning engineers

This course dives deeper into the field of Natural Language Generation (NLG), focusing on more advanced techniques such as transformer-based models, fine-tuning large pre-trained models, and evaluating NLG outputs. It provides intermediate-level participants with hands-on experience in implementing and optimizing NLG models for various applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to enhance their skills in generating high-quality, human-like text using advanced NLG methods.

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

• Understand advanced techniques for generating natural language text.
• Implement and fine-tune transformer-based models for NLG.
• Optimize NLG outputs for fluency, coherence, and relevance.
• Evaluate the quality of generated text using both automated and human metrics.

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.

Advanced NLG Techniques Overview

• Revisiting basic NLG concepts
• Introduction to advanced NLG methods
• Role of transformers in modern NLG

Pre-trained Models for NLG

• Overview of popular pre-trained models (GPT, BERT, T5)
• Fine-tuning pre-trained models for specific tasks
• Training custom models with large datasets

Improving NLG Outputs

• Handling coherence and relevance in text generation
• Controlling text length and content using NLG methods
• Techniques for reducing repetition and improving fluency

Ethical and Responsible NLG

• Understanding the ethical challenges of AI-generated content
• Dealing with biases in NLG models
• Ensuring the responsible use of NLG technology

Hands-On with Advanced NLG Libraries

• Working with Hugging Face Transformers for NLG
• Implementing GPT-3 and other state-of-the-art models
• Generating domain-specific content using NLG

Evaluating NLG Systems

• Techniques for evaluating NLG models
• Automated evaluation metrics (BLEU, ROUGE, METEOR)
• Human evaluation methods for quality assurance

Future Trends in NLG

• Emerging techniques in NLG research
• Challenges and opportunities in NLG development
• Impact of NLG on industries and content creation

Summary and Next Steps