  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.
