Exploring Learnability in Memory-Augmented Recurrent Neural Networks: Precision, Stability, and Empirical Insights

Recurrent Neural Networks (RNNs) have been foundational in sequence modeling due to their ability to capture temporal dependencies. Architectures such as Elman RNNs, Gated Recurrent Units (GRUs), and Long Short-Term Memory networks (LSTMs) [1] are widely used in applications like speech recognition, machine translation, and time-series analysis. However, these models are constrained by their fixed memory capacity, limiting them to recognizing regular languages when implemented with finite precision [2, 3]. To enhance the computational capabilities of RNNs, researchers have explored augmenting them with external memory structures like stacks [4, 5, 6, 7, 8, 9, 10]. This approach extends the expressivity of RNNs to context-free languages (CFLs) [11], which are crucial in applications like natural language processing (NLP) where hierarchical structures are prevalent. Memory-augmented models have demonstrated significant improvements in recognizing complex formal languages by simulating operations similar to Pushdown Automata (PDA).