Comparing Styles across Languages: A Cross-Cultural Exploration of Politeness
This work addresses the challenge of generating culturally appropriate text for training humans and computers, though it appears incremental in applying existing methods to a new domain.
The authors tackled the problem of understanding stylistic differences across languages by introducing an explanation framework that extracts stylistic differences from multilingual language models and compares styles across languages, creating the first holistic multilingual politeness dataset and exploring politeness variations across four languages.
Understanding how styles differ across languages is advantageous for training both humans and computers to generate culturally appropriate text. We introduce an explanation framework to extract stylistic differences from multilingual LMs and compare styles across languages. Our framework (1) generates comprehensive style lexica in any language and (2) consolidates feature importances from LMs into comparable lexical categories. We apply this framework to compare politeness, creating the first holistic multilingual politeness dataset and exploring how politeness varies across four languages. Our approach enables an effective evaluation of how distinct linguistic categories contribute to stylistic variations and provides interpretable insights into how people communicate differently around the world.