  In order to extract event information from text, a machine reading model must
learn to accurately read and interpret the ways in which that information is
expressed. But it must also, as the human reader must, aggregate numerous
individual value hypotheses into a single coherent global analysis, applying
global constraints which reflect prior knowledge of the domain.
  In this work we focus on the task of extracting plane crash event information
from clusters of related news articles whose labels are derived via distant
supervision. Unlike previous machine reading work, we assume that while most
target values will occur frequently in most clusters, they may also be missing
or incorrect.
  We introduce a novel neural architecture to explicitly model the noisy nature
of the data and to deal with these aforementioned learning issues. Our models
are trained end-to-end and achieve an improvement of more than 12.1 F$_1$ over
previous work, despite using far less linguistic annotation. We apply factor
graph constraints to promote more coherent event analyses, with belief
propagation inference formulated within the transitions of a recurrent neural
network. We show this technique additionally improves maximum F$_1$ by up to
2.8 points, resulting in a relative improvement of $50\%$ over the previous
state-of-the-art.

Computation and Language