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

TL;DR

This paper investigates the learnability of memory-augmented RNNs, showing that freezing memory components enhances stability and performance on long sequences, with implications for model design and evaluation.

Contribution

It introduces the empirical benefit of freezing memory in RNNs, demonstrating improved generalization and stability on long sequences, supported by theoretical analysis.

Findings

Freezing memory improves long-sequence performance.

State-of-the-art perplexity achieved on Penn Treebank.

Memory freezing stabilizes temporal dependencies.

Abstract

This study explores the learnability of memory-less and memory-augmented RNNs, which are theoretically equivalent to Pushdown Automata. Empirical results show that these models often fail to generalize on longer sequences, relying more on precision than mastering symbolic grammar. Experiments on fully trained and component-frozen models reveal that freezing the memory component significantly improves performance, achieving state-of-the-art results on the Penn Treebank dataset (test perplexity reduced from 123.5 to 120.5). Models with frozen memory retained up to 90% of initial performance on longer sequences, compared to a 60% drop in standard models. Theoretical analysis suggests that freezing memory stabilizes temporal dependencies, leading to robust convergence. These findings stress the need for stable memory designs and long-sequence evaluations to understand RNNs true learnability limits.