Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes

TL;DR

This paper introduces a dynamic neural Turing machine (D-NTM) with trainable memory addressing that can learn diverse location-based strategies, improving performance on various sequence tasks.

Contribution

The paper proposes a novel D-NTM with separate content and address vectors, enabling flexible addressing strategies and supporting both continuous and discrete read/write mechanisms.

Findings

D-NTM outperforms NTM and LSTM on Facebook bAbI tasks.

The model effectively learns diverse addressing strategies.

Extensive analysis demonstrates the model's versatility across tasks.

Abstract

We extend neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing a trainable memory addressing scheme. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing strategies including both linear and nonlinear ones. We implement the D-NTM with both continuous, differentiable and discrete, non-differentiable read/write mechanisms. We investigate the mechanisms and effects of learning to read and write into a memory through experiments on Facebook bAbI tasks using both a feedforward and GRUcontroller. The D-NTM is evaluated on a set of Facebook bAbI tasks and shown to outperform NTM and LSTM baselines. We have done extensive analysis of our model and different variations of NTM on bAbI task. We also provide further experimental results on sequential pMNIST, Stanford Natural Language Inference, associative recall and copy tasks.