TY - GEN
T1 - NORMSAGE
T2 - 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023
AU - Fung, Yi R.
AU - Charkaborty, Tuhin
AU - Guo, Hao
AU - Rambow, Owen
AU - Muresan, Smaranda
AU - Ji, Heng
N1 - This research is based upon work supported by U.S. DARPA CCU Program No. HR001122C0034. The opinions, views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
PY - 2023
Y1 - 2023
N2 - 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, NORMSAGE1, 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.
AB - 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, NORMSAGE1, 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.
UR - https://www.scopus.com/pages/publications/85175230538
UR - https://www.scopus.com/pages/publications/85175230538#tab=citedBy
U2 - 10.18653/v1/2023.emnlp-main.941
DO - 10.18653/v1/2023.emnlp-main.941
M3 - Conference contribution
AN - SCOPUS:85175230538
T3 - EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 15217
EP - 15230
BT - EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
A2 - Bouamor, Houda
A2 - Pino, Juan
A2 - Bali, Kalika
PB - Association for Computational Linguistics (ACL)
Y2 - 6 December 2023 through 10 December 2023
ER -