Culture is Not Trivia: Sociocultural Theory for Cultural NLP
This work addresses theoretical gaps in cultural NLP to improve language technologies for diverse users, but it is incremental as it builds on existing sociocultural theory.
The paper identifies that cultural NLP lacks a shared conception of culture, relying on proxies that lead to limitations like coarse national boundaries and static benchmarks, and proposes using sociocultural theory to address these issues through methodological clarification and a localization framing.
The field of cultural NLP has recently experienced rapid growth, driven by a pressing need to ensure that language technologies are effective and safe across a pluralistic user base. This work has largely progressed without a shared conception of culture, instead choosing to rely on a wide array of cultural proxies. However, this leads to a number of recurring limitations: coarse national boundaries fail to capture nuanced differences that lay within them, limited coverage restricts datasets to only a subset of usually highly-represented cultures, and a lack of dynamicity results in static cultural benchmarks that do not change as culture evolves. In this position paper, we argue that these methodological limitations are symptomatic of a theoretical gap. We draw on a well-developed theory of culture from sociocultural linguistics to fill this gap by 1) demonstrating in a case study how it can clarify methodological constraints and affordances, 2) offering theoretically-motivated paths forward to achieving cultural competence, and 3) arguing that localization is a more useful framing for the goals of much current work in cultural NLP.