Cultural Value Resonance in Folktales: A Transformer-Based Analysis with the World Value Corpus

Although implicit cultural values are reflected in human narrative texts, few robust computational solutions exist to recognize values that resonate within these texts. In other words, given a statement text and a value text, the task is to predict the label that resonates, conflicts or is neutral with respect to the value. In this paper, we present a novel, annotated dataset and transformer-based model for Recognizing Value Resonance (RVR). We created the World Values Corpus (WVC): a labeled collection of [statement, value] pairs of text based on the World Values Survey (WVS), which is a well-validated, comprehensive survey for assessing values across cultures. Each pair expresses whether the value resonates with, conflicts with, or is neutral to the statement. The 384 values in the WVC are derived from the WVS to assure the WVC's crosscultural relevance. The statement pairs for each value were generated by a pool of six annotators across genders and cultural backgrounds. We demonstrate that off-the-shelf Recognizing Textual Entailment (RTE) models perform unfavorably on the RVR task. However, RTE models trained on the WVC achieve substantially higher accuracy on RVR, serving as a strong, replicable baseline for future RVR work, advancing the study of cultural values using computational NLP approaches. We also present results of applying our baseline model on the "World of Tales" corpus, an online repository of international folktales. The results suggest that such a model can provide useful anthropological insights, which in turn is an important step towards facilitating automated ethnographic modeling.