NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery

Farhad Moghimifar, Shilin Qu, Tongtong Wu, Yuan-Fang Li, Gholamreza Haffari

Abstract
Norms, which are culturally accepted guidelines for behaviours, can be integrated into conversational models to generate utterances that are appropriate for the socio-cultural context. Existing methods for norm recognition tend to focus only on surface-level features of dialogues and do not take into account the interactions within a conversation. To address this issue, we propose NormMark, a probabilistic generative Markov model to carry the latent features throughout a dialogue. These features are captured by discrete and continuous latent variables conditioned on the conversation history, and improve the model’s ability in norm recognition. The model is trainable on weakly annotated data using the variational technique. On a dataset with limited norm annotations, we show that our approach achieves higher F1 score, outperforming current state-of-the-art methods, including GPT3.
Anthology ID:
2023.findings-acl.314
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5081–5089
Language:
URL:
https://aclanthology.org/2023.findings-acl.314
DOI:
10.18653/v1/2023.findings-acl.314
Bibkey:
Cite (ACL):
Farhad Moghimifar, Shilin Qu, Tongtong Wu, Yuan-Fang Li, and Gholamreza Haffari. 2023. NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5081–5089, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery (Moghimifar et al., Findings 2023)
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