@inproceedings{huang-yang-2023-culturally,
title = "Culturally Aware Natural Language Inference",
author = "Huang, Jing and
Yang, Diyi",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.509",
doi = "10.18653/v1/2023.findings-emnlp.509",
pages = "7591--7609",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Culturally Aware Natural Language Inference
%A Huang, Jing
%A Yang, Diyi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F huang-yang-2023-culturally
%X 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.
%R 10.18653/v1/2023.findings-emnlp.509
%U https://aclanthology.org/2023.findings-emnlp.509
%U https://doi.org/10.18653/v1/2023.findings-emnlp.509
%P 7591-7609
Markdown (Informal)
[Culturally Aware Natural Language Inference](https://aclanthology.org/2023.findings-emnlp.509) (Huang & Yang, Findings 2023)
ACL
- Jing Huang and Diyi Yang. 2023. Culturally Aware Natural Language Inference. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7591–7609, Singapore. Association for Computational Linguistics.