Text Generation using Long Short-Term Memory

Character-based generation models benefit from the application of Long Short-Term Memory (LSTM) cells. In handling sequential data, Single-Dimensional Convolutional LSTM networks also exhibit usefulness. The impact of the quantity of LSTM cells on the generated content quality is substantial, affecting the model's tendency towards overfitting or underfitting. Our investigation into the influence of LSTM cell quantity reveals an initial enhancement in predictive performance with increased cell numbers. However, surpassing a specific threshold of LSTM cells leads to a decline in model effectiveness. Optimizing the corpus vocabulary size contributes to superior outcomes when reduced.