  We propose PeTra, a memory-augmented neural network designed to track
entities in its memory slots. PeTra is trained using sparse annotation from the
GAP pronoun resolution dataset and outperforms a prior memory model on the task
while using a simpler architecture. We empirically compare key modeling
choices, finding that we can simplify several aspects of the design of the
memory module while retaining strong performance. To measure the people
tracking capability of memory models, we (a) propose a new diagnostic
evaluation based on counting the number of unique entities in text, and (b)
conduct a small scale human evaluation to compare evidence of people tracking
in the memory logs of PeTra relative to a previous approach. PeTra is highly
effective in both evaluations, demonstrating its ability to track people in its
memory despite being trained with limited annotation.

Computation and Language;Machine Learning