No Permanent Friends or Enemies: Tracking Relationships between Nations from News
This work addresses the challenge for civilians in understanding international politics by providing an unsupervised method to track nation relationships from news, though it is incremental in nature.
The authors tackled the problem of inferring international relations from news articles by extending unsupervised neural models with shallow linguistics and proposing a new automatic evaluation metric aligned with key events. Their model was preferred by humans in evaluations and revealed regional differences in news coverage, such as Singaporean media focusing on 'strengthening' and US media on 'criticizing' for US-China relations.
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".