Program Objectives

• Master the fundamentals of NLG and its various architectures.
• Build and train models for text generation using state-of-the-art techniques.
• Understand the ethical challenges and biases in NLG.
• Gain hands-on experience with transformer-based models for NLG tasks.
• Explore real-world applications of NLG in content automation and communication.

Program Structure

Module 1: Introduction to Natural Language Generation

• Overview of NLG
• Applications and Trends in NLG (e.g., Chatbots, Content Generation)
• Key Challenges in NLG

Module 2: Fundamentals of Natural Language Processing (NLP)

• Tokenization, Lemmatization, and Stemming
• Word Embeddings (Word2Vec, GloVe)
• Sequence Modeling in NLP (Bag of Words, TF-IDF)

Module 3: Recurrent Neural Networks (RNNs) for NLG

• RNNs and Sequence-to-Sequence Models
• LSTMs and GRUs for Text Generation
• Encoder-Decoder Architectures

Module 4: Transformers for Language Modeling

• Attention Mechanism and Self-Attention
• Introduction to Transformers
• BERT, GPT, and Their Role in NLG

Module 5: Advanced Language Models

• GPT-2, GPT-3, and GPT-4 Architectures
• Pretraining and Fine-tuning Techniques
• Comparison of Pretrained Language Models (e.g., T5, BART)

Module 6: Conditional NLG

• Text Generation with Conditional Inputs (e.g., Text Summarization, Translation)
• Seq2Seq with Attention
• Applications in Machine Translation (MT) and Summarization

Module 7: Controlling Text Generation

• Controlling Style and Tone in NLG
• Beam Search, Greedy Search, and Sampling Methods
• Top-k and Top-p Sampling

Module 8: Evaluating NLG Models

• Evaluation Metrics for NLG (BLEU, ROUGE, METEOR)
• Human Evaluation vs. Automated Evaluation
• Challenges in Evaluating Generated Text

Module 9: Ethics in NLG

• Bias and Fairness in Language Models
• Ethical Considerations in Text Generation
• Misinformation and Abuse of NLG Systems

Module 10: Fine-Tuning and Deploying NLG Models

• Fine-Tuning Large Language Models for Specific Domains
• Model Deployment in Real-World Applications
• Scaling and Optimizing NLG Models

Module 11: Case Studies in NLG

• Hands-on Applications (Chatbots, Automated Report Writing)
• Industry Use Cases (Marketing, Healthcare, Journalism)

Module 12: Final Project

• Build and deploy an NLG model for a specific task (e.g., text summarization, chatbot, or creative writing generator)