@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery
%A Moghimifar, Farhad
%A Qu, Shilin
%A Wu, Tongtong
%A Li, Yuan-Fang
%A Haffari, Gholamreza
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F moghimifar-etal-2023-normmark
%X 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.
%R 10.18653/v1/2023.findings-acl.314
%U https://aclanthology.org/2023.findings-acl.314
%U https://doi.org/10.18653/v1/2023.findings-acl.314
%P 5081-5089
Markdown (Informal)
[NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery](https://aclanthology.org/2023.findings-acl.314) (Moghimifar et al., Findings 2023)
ACL