Neural Language Generation - Models and Applications: Studying models and applications of neural language generation for tasks such as text generation, dialogue generation, and story generation

Abstract

Neural Language Generation (NLG) has emerged as a transformative technology in natural language processing (NLP), enabling machines to generate human-like text. This paper provides a comprehensive overview of NLG models and their applications, focusing on text generation, dialogue generation, and story generation. We discuss the evolution of NLG from rule-based approaches to modern deep learning models, including recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformer models. We also examine key challenges such as coherence, diversity, and controllability in NLG and explore how these challenges are addressed in state-of-the-art models. Furthermore, we review various applications of NLG across different domains, highlighting their impact on tasks such as language translation, content generation, and human-computer interaction. Finally, we discuss future directions and emerging trends in NLG research, emphasizing the potential for further advancements in generating human-like text.