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”.- Anthology ID:
- N19-1167
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1660–1676
- Language:
- URL:
- https://aclanthology.org/N19-1167
- DOI:
- 10.18653/v1/N19-1167
- Cite (ACL):
- Xiaochuang Han, Eunsol Choi, and Chenhao Tan. 2019. No Permanent Friends or Enemies: Tracking Relationships between Nations from News. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1660–1676, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- No Permanent Friends or Enemies: Tracking Relationships between Nations from News (Han et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/N19-1167.pdf
- Code
- BoulderDS/LARN