  Identifying temporal relations between events is an essential step towards
natural language understanding. However, the temporal relation between two
events in a story depends on, and is often dictated by, relations among other
events. Consequently, effectively identifying temporal relations between events
is a challenging problem even for human annotators. This paper suggests that it
is important to take these dependencies into account while learning to identify
these relations and proposes a structured learning approach to address this
challenge. As a byproduct, this provides a new perspective on handling missing
relations, a known issue that hurts existing methods. As we show, the proposed
approach results in significant improvements on the two commonly used data sets
for this problem.
