  Understanding the dynamics of international politics is important yet
challenging for civilians. In this work, we explore unsupervised neural models
to infer relations between nations from news articles. We extend existing
models by incorporating shallow linguistics information and propose a new
automatic evaluation metric that aligns relationship dynamics with manually
annotated key events. As understanding international relations requires
carefully analyzing complex relationships, we conduct in-person human
evaluations with three groups of participants. Overall, humans prefer the
outputs of our model and give insightful feedback that suggests future
directions for human-centered models. Furthermore, our model reveals
interesting regional differences in news coverage. For instance, with respect
to US-China relations, Singaporean media focus more on "strengthening" and
"purchasing", while US media focus more on "criticizing" and "denouncing".
