Memory-Augmented Neural Networks for Machine Translation

Understand

Memory-augmented neural networks (MANNs) have been shown to outperform other recurrent neural network architectures on a series of artificial sequence learning tasks, yet they have had limited application to real-world tasks.

• We evaluate direct application of Neural Turing Machines (NTM) and Differentiable Neural Computers (DNC) to machine translation.
• We further propose and evaluate two models which extend the attentional encoder-decoder with capabilities inspired by memory augmented neural networks.
• We evaluate our proposed models on IWSLT Vietnamese to English and ACL Romanian to English datasets.