Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis

Shrey Desai, Barea Sinno, Alex Rosenfeld, Junyi Jessy Li


Abstract
Insightful findings in political science often require researchers to analyze documents of a certain subject or type, yet these documents are usually contained in large corpora that do not distinguish between pertinent and non-pertinent documents. In contrast, we can find corpora that label relevant documents but have limitations (e.g., from a single source or era), preventing their use for political science research. To bridge this gap, we present adaptive ensembling, an unsupervised domain adaptation framework, equipped with a novel text classification model and time-aware training to ensure our methods work well with diachronic corpora. Experiments on an expert-annotated dataset show that our framework outperforms strong benchmarks. Further analysis indicates that our methods are more stable, learn better representations, and extract cleaner corpora for fine-grained analysis.
Anthology ID:
D19-1478
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4718–4730
Language:
URL:
https://aclanthology.org/D19-1478
DOI:
10.18653/v1/D19-1478
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/D19-1478.pdf
Attachment:
 D19-1478.Attachment.pdf
Code
 shreydesai/adaptive-ensembling