Cultural Bias Matters: A Cross-Cultural Benchmark Dataset and Sentiment-Enriched Model for Understanding Multimodal Metaphors
Journal:
arXiv
Published Date:
Jun 8, 2025
Abstract
Metaphors are pervasive in communication, making them crucial for natural
language processing (NLP). Previous research on automatic metaphor processing
predominantly relies on training data consisting of English samples, which
often reflect Western European or North American biases. This cultural skew can
lead to an overestimation of model performance and contributions to NLP
progress. However, the impact of cultural bias on metaphor processing,
particularly in multimodal contexts, remains largely unexplored. To address
this gap, we introduce MultiMM, a Multicultural Multimodal Metaphor dataset
designed for cross-cultural studies of metaphor in Chinese and English. MultiMM
consists of 8,461 text-image advertisement pairs, each accompanied by
fine-grained annotations, providing a deeper understanding of multimodal
metaphors beyond a single cultural domain. Additionally, we propose
Sentiment-Enriched Metaphor Detection (SEMD), a baseline model that integrates
sentiment embeddings to enhance metaphor comprehension across cultural
backgrounds. Experimental results validate the effectiveness of SEMD on
metaphor detection and sentiment analysis tasks. We hope this work increases
awareness of cultural bias in NLP research and contributes to the development
of fairer and more inclusive language models. Our dataset and code are
available at https://github.com/DUTIR-YSQ/MultiMM.
