  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.
