Comparing Styles across Languages

Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle Ungar

Submission Type: Regular Long Paper

Submission Track: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Submission Track 2: Computational Social Science and Cultural Analytics

Keywords: NLP, Style, Cross-Cultural, Multilingual, Explainability, Lexica

TL;DR: We introduce an explanation framework to extract stylistic differences from multilingual LMs and compare style across languages.

Abstract: 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.

Submission Number: 1147