The cultural landscape of interactions with dialogue agents is a compelling yet relatively unexplored territory. It's clear that various sociocultural aspects -- from communication styles and beliefs to shared metaphors and knowledge -- profoundly impact these interactions. To delve deeper into this dynamic, we introduce cuDialog, a first-of-its-kind benchmark for dialogue generation with a cultural lens. We also develop baseline models capable of extracting cultural attributes from dialogue exchanges, with the goal of enhancing the predictive accuracy and quality of dialogue agents. To effectively co-learn cultural understanding and multi-turn dialogue predictions, we propose to incorporate cultural dimensions with dialogue encoding features. Our experimental findings highlight that incorporating cultural value surveys boosts alignment with references and cultural markers, demonstrating its considerable influence on personalization and dialogue quality. To facilitate further exploration in this exciting domain, we publish our benchmark publicly accessible at https://github.com/yongcaoplus/cuDialog.

翻译：对话代理交互的文化景观是一个引人入胜但相对未被充分探索的领域。显而易见，从沟通风格、信念到共享隐喻和知识等各类社会文化因素，深刻影响着这些交互。为深入探究这一动态，我们引入了cuDialog——首个具备文化视角的对话生成基准测试。我们还开发了基线模型，能够从对话交换中提取文化属性，旨在提升对话代理的预测准确性和质量。为有效协同学习文化理解与多轮对话预测，我们提出将文化维度与对话编码特征相结合。我们的实验结果表明，融入文化价值观调查可增强与参考文本及文化标志的对齐性，彰显了其对个性化和对话质量的显著影响。为促进这一激动人心领域的进一步探索，我们在https://github.com/yongcaoplus/cuDialog上公开发布了该基准测试。