To this end, we design a neural network architecture, capable of tracking and updating the states of entities at the right time with external memory, making it a natural fit for the task.
our model maintains a number of "memory chains" h j , one for each entity with the key k j and dynamically updates the states (h j ) of them as it progresses through the sentence with the help of the delay recurrence d j , taking previous activations into account.