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How to teach machines to read human rights reports and identify judgments at scale

Abstract The accelerating availability of information from human rights monitors such as Amnesty International, Human Rights Watch, and the US State Department has led to new opportunities to measure repression and human rights protections in higher resolution. However, to date, most approaches that attempt to automatically structure textual reports use simple, lower-dimensional observations such as the counts of words that ignore syntax and word order. While these representations are useful for some applications, they limit the inferences scholars and policy-makers can extract from human rights reports. In this article, we present a new system, PULSAR, that takes syntax and word order into account. This system uniquely allows researchers to extract both the judgements and the aspects/rights being judged from texts at scale. We illustrate that this more detailed information is useful both for improving predictions of physical integrity rights and women's political rights, but also for generating machine learning models that are more interpretable than conventional specifications. This latter benefit holds the promise of coherently connecting qualitative and quantitative analyses of human rights texts.