Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys

TL;DR

This paper introduces cuDialog, a benchmark for culturally-aware dialogue generation, and demonstrates that incorporating cultural value surveys improves dialogue alignment and personalization.

Contribution

The paper presents the first benchmark for cultural dialogue generation and baseline models that incorporate cultural attributes to enhance dialogue quality.

Findings

Cultural value surveys improve dialogue reference alignment.

Incorporating cultural dimensions enhances personalization.

Baseline models effectively extract cultural attributes.

Abstract

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.