Analysis reveals trends in cultural capabilities and data for nlp systems
CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems
Computation and Language
Summary
Natural language processing (NLP) systems that understand culture are growing fast, but it’s hard to keep track of progress. The authors studied over 375 papers to find out which cultural abilities NLP systems focus on, how cultural datasets are made, and the methods used to improve these systems. They also created an interactive website listing all those papers to help others follow and contribute to this research area. Their work highlights current trends and gaps to guide future NLP development with better cultural understanding.
What this means in practice
- •For nlp developers: Use a curated and growing database of cultural NLP papers and resources to choose relevant cultural capabilities and methods for new systems.
- •For product managers in global tech: Identify gaps in cultural NLP techniques to prioritize features that improve system inclusivity for diverse user bases.
A survey. It maps existing work.
Authors
Tania Chakraborty, Eylon Caplan, Zhaoqing Wu, Kevin Cushing, Han Qin, Shreya Havaldar, Dan Goldwasser
Abstract
In recent years, there has been a surge of interest in Cultural NLP, with substantial efforts to create globally inclusive NLP systems. The rapid growth of literature in this field makes it difficult to track trends in methods and data resources. To address this, we analyze over 375 papers to answer three complementary questions: (1) What Cultural Capabilities (CCs) are being targeted in NLP systems? (2) How are cultural data resources being created? and (3) What methods are being used to improve the CCs of those systems? We discuss trends observed across the three questions, and identify relevant research gaps. To facilitate further research in this field, we release our full list of analyzed papers in the form of an interactive web interface, which includes a feature to allow researchers to add their work; we hope this facilitates future research and proves to be a valuable resource for the Cultural NLP community.