没有永远的朋友或敌人：从新闻追踪国家之间的关系
No Permanent Friends or Enemies: Tracking Relationships between Nations from News
摘要：
了解国际政治的动态对平民来说很重要，但也很有挑战性。在这项工作中，我们探索了无监督的神经模型，以从新闻文章中推断国家之间的关系。我们扩展了现有的模型，将浅语言学信息，并提出了一个新的自动评估指标，调整关系动态与手动注释的关键事件。由于理解国际关系需要仔细分析复杂的关系，我们与三组参与者进行了面对面的人类评估。总的来说，人类更喜欢我们模型的输出，并给出有见地的反馈，为以人为中心的模型提出未来的发展方向。此外，我们的模型揭示了有趣的区域差异的新闻报道。例如，在美中关系方面，新加坡媒体更注重“加强”和“购买”，而美国媒体则更注重“批评”和“谴责”。
英文摘要：
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".
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
