Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys
This work addresses the challenge of personalization and quality in dialogue agents for diverse cultural contexts, representing an incremental advance in a relatively unexplored area.
The authors tackled the problem of cultural nuances in dialogue agents by introducing cuDialog, a benchmark for culturally-aware dialogue generation, and found that incorporating cultural value surveys improved alignment with references and cultural markers.
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.