Year: 2024
Type: conference-paper
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 knowledgeprofoundly impact these interactions.To delve deeper into this dynamic, we introduce cu-Dialog, 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.
Authors: Yong Cao, Min Chen, Daniel Hershcovich
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7126402292