Description

• Define Natural Language Generation (NLG) • Identify the key components of NLG systems • Explain the role of syntax, semantics, and pragmatics in NLG • Understand the importance of grammar in NLG • Explore data sources for NLG • Understand techniques for data pre-processing in NLG • Differentiate between rule-based, machine learning, and deep learning NLG approaches • Explore NLG algorithm applications in real-world scenarios • Apply knowledge gained in previous modules to a real-world project

Program

Artificial Intelligence 132 CRs

Objectives

• This course focuses on computer systems producing human-like text. Beyond chatbots, this course delves into story generation, translation, and automated journalism. Students will dissect state-of- the-art models like Transformers, explore techniques such as attention mechanisms, and tackle challenges like coherence and context retention. Real-world projects will enhance understanding and showcase applications across industries.