@inproceedings{fung-etal-2023-normsage,
title = "{NORMSAGE}: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly",
author = "Fung, Yi and
Chakrabarty, Tuhin and
Guo, Hao and
Rambow, Owen and
Muresan, Smaranda and
Ji, Heng",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.941",
doi = "10.18653/v1/2023.emnlp-main.941",
pages = "15217--15230",
abstract = "Knowledge of norms is needed to understand and reason about acceptable behavior in human communication and interactions across sociocultural scenarios. Most computational research on norms has focused on a single culture, and manually built datasets, from non-conversational settings. We address these limitations by proposing a new framework, NormSage, to automatically extract culture-specific norms from multi-lingual conversations. NormSage uses GPT-3 prompting to 1) extract candidate norms directly from conversations and 2) provide explainable self-verification to ensure correctness and relevance. Comprehensive empirical results show the promise of our approach to extract high-quality culture-aware norms from multi-lingual conversations (English and Chinese), across several quality metrics. Further, our relevance verification can be extended to assess the adherence and violation of any norm with respect to a conversation on-the-fly, along with textual explanation. NormSage achieves an AUC of 94.6{\%} in this grounding setup, with generated explanations matching human-written quality.",
}
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<abstract>Knowledge of norms is needed to understand and reason about acceptable behavior in human communication and interactions across sociocultural scenarios. Most computational research on norms has focused on a single culture, and manually built datasets, from non-conversational settings. We address these limitations by proposing a new framework, NormSage, to automatically extract culture-specific norms from multi-lingual conversations. NormSage uses GPT-3 prompting to 1) extract candidate norms directly from conversations and 2) provide explainable self-verification to ensure correctness and relevance. Comprehensive empirical results show the promise of our approach to extract high-quality culture-aware norms from multi-lingual conversations (English and Chinese), across several quality metrics. Further, our relevance verification can be extended to assess the adherence and violation of any norm with respect to a conversation on-the-fly, along with textual explanation. NormSage achieves an AUC of 94.6% in this grounding setup, with generated explanations matching human-written quality.</abstract>
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%0 Conference Proceedings
%T NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly
%A Fung, Yi
%A Chakrabarty, Tuhin
%A Guo, Hao
%A Rambow, Owen
%A Muresan, Smaranda
%A Ji, Heng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F fung-etal-2023-normsage
%X Knowledge of norms is needed to understand and reason about acceptable behavior in human communication and interactions across sociocultural scenarios. Most computational research on norms has focused on a single culture, and manually built datasets, from non-conversational settings. We address these limitations by proposing a new framework, NormSage, to automatically extract culture-specific norms from multi-lingual conversations. NormSage uses GPT-3 prompting to 1) extract candidate norms directly from conversations and 2) provide explainable self-verification to ensure correctness and relevance. Comprehensive empirical results show the promise of our approach to extract high-quality culture-aware norms from multi-lingual conversations (English and Chinese), across several quality metrics. Further, our relevance verification can be extended to assess the adherence and violation of any norm with respect to a conversation on-the-fly, along with textual explanation. NormSage achieves an AUC of 94.6% in this grounding setup, with generated explanations matching human-written quality.
%R 10.18653/v1/2023.emnlp-main.941
%U https://aclanthology.org/2023.emnlp-main.941
%U https://doi.org/10.18653/v1/2023.emnlp-main.941
%P 15217-15230
Markdown (Informal)
[NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly](https://aclanthology.org/2023.emnlp-main.941) (Fung et al., EMNLP 2023)
ACL