文化偏见至关重要：用于理解多模态隐喻的跨文化基准数据集与情感增强模型
Cultural Bias Matters: A Cross-Cultural Benchmark Dataset and Sentiment-Enriched Model for Understanding Multimodal Metaphors
AI总结：
针对多模态隐喻研究中的英语文化偏见，本文构建含8,461对图文广告的中英跨文化数据集MultiMM，并提出融合情感嵌入的SEMD基线模型。
摘要：
隐喻在交流中无处不在，因此对自然语言处理（NLP）至关重要。以往关于自动隐喻处理的研究主要依赖由英语样本构成的训练数据，这些数据往往反映西欧或北美的偏见。这种文化偏斜可能导致对模型性能及其对NLP进展贡献的高估。然而，文化偏见对隐喻处理的影响，尤其是在多模态语境中，仍基本未被探索。为弥补这一空白，我们推出MultiMM，这是一个多文化多模态隐喻数据集，旨在用于中文和英文隐喻的跨文化研究。MultiMM包含8,461个文本-图像广告对，每对都配有细粒度标注，从而提供超越单一文化领域的、对多模态隐喻更深入的理解。此外，我们提出情感增强隐喻检测（SEMD），这是一种整合情感嵌入以增强跨文化背景下隐喻理解的基线模型。实验结果验证了SEMD在隐喻检测和情感分析任务上的有效性。我们希望这项工作能提高人们对NLP研究中文化偏见的认识，并推动更公平、更具包容性的语言模型发展。我们的数据集和代码可在https://github.com/DUTIR-YSQ/MultiMM获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
