Few-Shot Upsampling for Protest Size Detection

Abstract

We propose a new task and dataset for a common problem in social science research: “upsampling” coarse document labels to fine-grained labels or spans. We pose the problem in a question answering format, with the answers providing the fine-grained labels. We provide a benchmark dataset and baselines on a socially impactful task: identifying the exact crowd size at protests and demonstrations in the United States given only order-of-magnitude information about protest attendance, a very small sample of fine-grained examples, and English-language news text. We evaluate several baseline models, including zero-shot results from rule-based and question-answering models, few-shot models fine-tuned on a small set of documents, and weakly supervised models using a larger set of coarsely-labeled documents. We find that our rule-based model initially outperforms a zero-shot pre-trained transformer language model but that further fine-tuning on a very small subset of 25 examples substantially improves out-of-sample performance. We also demonstrate a method for fine-tuning the transformer span on only the coarse labels that performs similarly to our rule-based approach. This work will contribute to social scientists’ ability to generate data to understand the causes and successes of collective action.

Publication
In Findings of the Association for Computational Linguistics
Andy Halterman
Andy Halterman
Faculty Fellow, NYU Center for Data Science

My research interests include natural language processing, text as data, and subnational armed conflict

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