Culturally Aware Natural Language Inference

Abstract

Humans produce and consume language in a particular cultural context, which includes knowledge about specific norms and practices. A listener's awareness of the cultural context is critical for interpreting the speaker's meaning. A simple expression like "I didn't leave a tip" implies a strong sense of dissatisfaction when tipping is assumed to be the norm. As NLP systems reach users from different cultures, achieving culturally aware language understanding becomes increasingly important. However, current research has focused on building cultural knowledge bases without studying how such knowledge leads to contextualized interpretations of texts. In this work, we operationalize cultural variations in language understanding through a natural language inference (NLI) task that surfaces cultural variations as label disagreement between annotators from different cultural groups. We introduce the first Culturally Aware Natural Language Inference (CALI) dataset with 2.7K premise-hypothesis pairs annotated by two cultural groups located in the U.S. and India. With CALI, we categorize how cultural norms affect language understanding and present an evaluation framework to assess at which levels large language models are culturally aware. Our dataset is available at https://github.com/ SALT-NLP/CulturallyAwareNLI.

Advanced Semantics for Commonsense Knowledge Extraction

“…Culture-aware NLP: Several benchmarks exist for tasks that involve cultural knowledge beyond the Western world, e.g., MaRVL (Liu et al, 2021), GD-VCR (Yin et al, 2021), GeoMLAMA (Yin et al, 2022, CALI (Huang and Yang, 2023), and FORK (Palta and Rudinger, 2023). Some methods (e.g., Zhang, 2023), (Zhou et al, 2023)) have used LLMs to solve tasks of this kind.…”