Comparative Study of Deep Neural Language Models for Text Generation

Natural Language Generation (NLG) has emerged as a leading artificial intelligence tool, particularly for text generation. This work examines deep neural language models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), and Gated Recurrent Units (GRU), from the standpoint of text synthesis. The study offers a comprehensive comparison of the effectiveness of each model, including their design, methods of training, and general performance in various text creation scenarios. Issues including vanishing gradients, comprehending the trade-offs between model complexity and performance, and determining the model's adaptation to other domains are all addressed throughout the examination. We investigate how well these models capture contextual subtleties and produce coherent, contextually relevant text across a wide range of applications, including chatbots, content generation, and summarization. We delve into the impact of model size, training data, and architectural nuances on the quality and efficiency of text generation. The results show that in terms of weighted average, accuracy, loss, and perplexity, GRU performs better than CNN, RNN, and LSTM.