No Permanent Friends or Enemies: Tracking Relationships between Nations from News

April 18, 2019 · Entered Twilight · 🏛 North American Chapter of the Association for Computational Linguistics

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Repo contents: README.md, baseline_model.ipynb, change_point_analysis.ipynb, constants.py, data_processing.ipynb, modules.py, our_model.ipynb, utils.py

Authors Xiaochuang Han, Eunsol Choi, Chenhao Tan arXiv ID 1904.08950 Category cs.CL: Computation & Language Cross-listed cs.AI, cs.SI Citations 11 Venue North American Chapter of the Association for Computational Linguistics Repository https://github.com/BoulderDS/LARN ⭐ 5 Last Checked 1 month ago

Abstract

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".