While human annotations play a crucial role in language technologies, annotator subjectivity has long been overlooked in data collection. Recent studies that critically examine this issue are often focused on Western contexts, and solely document differences across age, gender, or racial groups. Consequently, NLP research on subjectivity have failed to consider that individuals within demographic groups may hold diverse values, which influence their perceptions beyond group norms. To effectively incorporate these considerations into NLP pipelines, we need datasets with extensive parallel annotations from a variety of social and cultural groups.In this paper we introduce the D3CODE dataset: a large-scale cross-cultural dataset of parallel annotations for offensive language in over 4.5K English sentences annotated by a pool of more than 4k annotators, balanced across gender and age, from across 21 countries, representing eight geo-cultural regions. The dataset captures annotators’ moral values along six moral foundations: care, equality, proportionality, authority, loyalty, and purity. Our analyses reveal substantial regional variations in annotators’ perceptions that are shaped by individual moral values, providing crucial insights for developing pluralistic, culturally sensitive NLP models.

Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models. However, existing datasets are limited in size and coverage, and are largely restricted to stereotypes prevalent in the Western society. This is especially problematic as language technologies gain hold across the globe. To address this gap, we present SeeGULL, a broad-coverage stereotype dataset, built by utilizing generative capabilities of large language models such as PaLM, and GPT-3, and leveraging a globally diverse rater pool to validate the prevalence of those stereotypes in society. SeeGULL is in English, and contains stereotypes about identity groups spanning 178 countries across 8 different geo-political regions across 6 continents, as well as state-level identities within the US and India. We also include fine-grained offensiveness scores for different stereotypes and demonstrate their global disparities. Furthermore, we include comparative annotations about the same groups by annotators living in the region vs. those that are based in North America, and demonstrate that within-region stereotypes about groups differ from those prevalent in North America.

Cognitive tests have traditionally resorted to standardizing testing materials in the name of equality and because of the onerous nature of creating test items. This approach ignores participants’ diverse language experiences that potentially significantly affect testing outcomes. Here, we seek to explain our prior finding of significant performance differences on two cognitive tests (reading span and SPiN) between clusters of participants based on their media consumption. Here, we model the language contained in these media sources using an LSTM trained on corpora of each cluster’s media sources to predict target words. We also model semantic similarity of test items with each cluster’s corpus using skip-thought vectors. We find robust, significant correlations between performance on the SPiN test and the LSTMs and skip-thought models we present here, but not the reading span test.