@inproceedings{moghimifar-etal-2023-normmark,
    title = "{N}orm{M}ark: A Weakly Supervised {M}arkov Model for Socio-cultural Norm Discovery",
    author = "Moghimifar, Farhad  and
      Qu, Shilin  and
      Wu, Tongtong  and
      Li, Yuan-Fang  and
      Haffari, Gholamreza",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.314",
    doi = "10.18653/v1/2023.findings-acl.314",
    pages = "5081--5089",
    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.",
}

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.
Moghimifar, Farhad Qu, Shilin Wu, Tongtong Li, Yuan-Fang Haffari, Gholamreza
NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery