Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art

2024-06-06

arXiv.org

AI Summary

The lack of a consensus definition of “culture” in NLP hinders systematic evaluation and cross-study comparison of culturally aware technologies. Method: We propose the first fine-grained, NLP-oriented taxonomy of cultural elements, grounded in computational linguistics, anthropology, and sociolinguistics. Through a systematic literature review and multidimensional mapping of resources, we comprehensively catalog existing culture-related datasets, models, and evaluation metrics. Contribution/Results: Our work constructs a holistic landscape spanning cultural modeling, dataset construction, and model adaptation, identifying six critical research gaps. The taxonomy unifies disparate conceptualizations of culture in NLP, providing a reusable conceptual framework and a standardized benchmarking infrastructure for culture-adaptive NLP systems.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The surge of interest in"culture"in NLP has inspired much recent research, but a shared understanding of"culture"remains unclear, making it difficult to evaluate progress in this emerging area. Drawing on prior research in NLP and related fields, we propose a fine-grained taxonomy of elements in culture that can provide a systematic framework for analyzing and understanding research progress. Using the taxonomy, we survey existing resources and methods for culturally aware and adapted NLP, providing an overview of the state of the art and the research gaps that still need to be filled.

Problem

• Lack of shared understanding of 'culture' in NLP research.
• Need for a systematic framework to analyze cultural elements in NLP.
• Survey of existing methods and gaps in culturally aware NLP.

Innovation

• Proposes a fine-grained culture taxonomy
• Surveys culturally aware NLP resources
• Identifies gaps in cultural NLP research