The article presents Pointer-Augmented Neural Memory (PANM), a novel approach aimed at enhancing neural networks' capabilities in symbol processing for longer data sequences. By integrating an external neural memory with pointer manipulation techniques, PANM can effectively learn to perform complex operations such as pointer assignment and arithmetic through end-to-end training, thus mimicking human-like processing abilities. Experimental results showcase significant improvements in performance for algorithmic tasks, including Dyck language recognition, mathematical reasoning, and machine translation, with PANM enabling Transformers to achieve impressive generalization accuracy in compositional learning tasks.
Our proposed Pointer-Augmented Neural Memory (PANM) is designed to bridge the gap in neural networks' ability to process longer sequences and perform symbol processing.
PANM's use of external neural memory and pointer manipulation lets it learn operations like pointer assignment and arithmetic, enhancing its adaptability to sequential models.
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