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 ...