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.

Surveys are widely used to collect patient data in healthcare, and there is significant clinical interest in predicting patient outcomes using survey data. However, surveys often include numerous features that lead to high-dimensional inputs for machine learning models. This paper exploits a unique source of information in surveys for feature selection. We observe that feature names (i.e., survey questions) are often semantically indicative of what features are most useful. Using language models, we leverage semantic textual similarity (STS) scores between features and targets to select features. The performance of STS scores in directly ranking features as well as in the minimal-redundancy-maximal-relevance (mRMR) algorithm is evaluated using survey data collected as part of a clinical study on persistent post-surgical pain (PPSP) as well as an accessible dataset collected through the NIH All of Us program. Our findings show that features selected with STS can result in higher performance models compared to traditional feature selection algorithms.