Beyond English: Evaluating Automated Measurement of Moral Foundations in Non-English Discourse with a Chinese Case Study
This addresses the limitation of moral foundation theory applications in non-English discourse, offering insights for researchers and practitioners in computational social science and multilingual NLP, though it is incremental in extending existing methods to new languages.
This study tackled the problem of measuring moral foundations in non-English texts, using Chinese as a case study, and found that multilingual models and large language models (LLMs) reliably perform cross-language assessments, with LLMs excelling in data efficiency, while machine translation and local lexicons often lose cultural information.
This study explores computational approaches for measuring moral foundations (MFs) in non-English corpora. Since most resources are developed primarily for English, cross-linguistic applications of moral foundation theory remain limited. Using Chinese as a case study, this paper evaluates the effectiveness of applying English resources to machine translated text, local language lexicons, multilingual language models, and large language models (LLMs) in measuring MFs in non-English texts. The results indicate that machine translation and local lexicon approaches are insufficient for complex moral assessments, frequently resulting in a substantial loss of cultural information. In contrast, multilingual models and LLMs demonstrate reliable cross-language performance with transfer learning, with LLMs excelling in terms of data efficiency. Importantly, this study also underscores the need for human-in-the-loop validation of automated MF assessment, as the most advanced models may overlook cultural nuances in cross-language measurements. The findings highlight the potential of LLMs for cross-language MF measurements and other complex multilingual deductive coding tasks.