  Reading comprehension tasks test the ability of models to process long-term
context and remember salient information. Recent work has shown that relatively
simple neural methods such as the Attention Sum-Reader can perform well on
these tasks; however, these systems still significantly trail human
performance. Analysis suggests that many of the remaining hard instances are
related to the inability to track entity-references throughout documents. This
work focuses on these hard entity tracking cases with two extensions: (1)
additional entity features, and (2) training with a multi-task tracking
objective. We show that these simple modifications improve performance both
independently and in combination, and we outperform the previous state of the
art on the LAMBADA dataset, particularly on difficult entity examples.
