Natural Language Generation (NLG)

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Mentor Based

Program Syllabus

Module 1

About This Course

This program offers a comprehensive exploration of NLG techniques, teaching participants how AI can automatically generate coherent and contextually accurate human language. The program covers language models, neural architectures, ethical considerations, and hands-on projects focused on implementing NLG in real-world applications like automated writing, chatbots, and content creation.

Module 2

Aim

To provide researchers, AI professionals, and PhD scholars with a deep understanding of Natural Language Generation (NLG), focusing on its architectures, applications, and challenges. This course will cover the theoretical foundations and practical aspects of generating human-like text using AI, from basic models to advanced systems like GPT and BERT.

Module 3

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.

Module 4

Program Structure

Introduction to Natural Language Generation
- Overview of NLG
- Applications and Trends in NLG (e.g., Chatbots, Content Generation)
- Key Challenges in NLG
Fundamentals of Natural Language Processing (NLP)
- Tokenization, Lemmatization, and Stemming
- Word Embeddings (Word2Vec, GloVe)
- Sequence Modeling in NLP (Bag of Words, TF-IDF)
Recurrent Neural Networks (RNNs) for NLG
- RNNs and Sequence-to-Sequence Models
- LSTMs and GRUs for Text Generation
- Encoder-Decoder Architectures
Transformers for Language Modeling
- Attention Mechanism and Self-Attention
- Introduction to Transformers
- BERT, GPT, and Their Role in NLG
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)
Conditional NLG
- Text Generation with Conditional Inputs (e.g., Text Summarization, Translation)
- Seq2Seq with Attention
- Applications in Machine Translation (MT) and Summarization
Controlling Text Generation
- Controlling Style and Tone in NLG
- Beam Search, Greedy Search, and Sampling Methods
- Top-k and Top-p Sampling
Evaluating NLG Models
- Evaluation Metrics for NLG (BLEU, ROUGE, METEOR)
- Human Evaluation vs. Automated Evaluation
- Challenges in Evaluating Generated Text
Ethics in NLG
- Bias and Fairness in Language Models
- Ethical Considerations in Text Generation
- Misinformation and Abuse of NLG Systems
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

Case Studies in NLG
- Hands-on Applications (Chatbots, Automated Report Writing)
- Industry Use Cases (Marketing, Healthcare, Journalism)
Final Project
- Build and deploy an NLG model for a specific task (e.g., text summarization, chatbot, or creative writing generator)

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.