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• W2962705396 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”." @default.
• W2962705396 created "2019-07-30" @default.
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• W2962705396 date "2019-01-01" @default.
• W2962705396 modified "2023-09-25" @default.
• W2962705396 title "No Permanent" @default.
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• W2962705396 doi "https://doi.org/10.18653/v1/n19-1167" @default.
• W2962705396 hasPublicationYear "2019" @default.
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