Hands-On Natural Language Processing with Python

Memory network pipeline overview

A generic memory network's architecture can be decomposed into four parts: a Question Module, an Input Module, a Memory Module, and an Output Module. As is common practice in neural networks, information passes from one module to the other through dense vectors/embeddings, making the parameters of the model end-to-end trainable using gradient descent:

The working of this model is as follows:

• The Input Module receives multiple facts and encodes each of them in vectors.
• The Question Module, similar to the Input Module, is responsible for encoding the question in a vector.
• The Memory Module receives the encoded ...