N orm M ark: A Weakly Supervised M arkov Model for Socio-cultural Norm Discovery (2023)

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.

## introduction norms can be thought of as pre-defined socioculturally acceptable boundaries for human behaviour @xcite , and incorporating them into conversational models helps to produce contextually, socially and culturally appropriate utterances. for instance, identifying the sociocultural norm of greeting in a negotiation helps to generate responses suitable for the power dynamics and social setting. whereas, failing to detect and adhere to such norms can negatively impact social interactions @xcite . recent advances in developing chatbots have also highlighted the necessity of incorporating such implicit socio-cultural information into machine-generated responses, in order to approximate human-like interactions @xcite . norm discovery is a nascent research problem, and current approaches @xcite heavily rely on manually constructed sets of rules from available resources such as reddit @xcite figure 1 : the heatmap of norm distribution based on norm label of the previous segment, constructed from ldc2022e20. unit in column j of row i shows the probability of norm i following norm j. 2020; @xcite . in addition to the time and cost inefficiency of such approaches, the construction and use of these banks of norms treat each sentence or segment in isolation, and they fail to take the dependencies between norms in the flow of a dialogue into account @xcite . for instance, it is most likely that a dialogue segment containing the norm of request follows a segment that includes request and criticism (figure 1 ). furthermore, such approaches require a large amount of annotated data, limiting their performance on sparsely labelled resources. to address these limitations, in this paper, we propose a deep generative markov model that captures the inter-dependencies between turns (segments) of partially-labelled dialogues. the model includes two types of latent variables (lvs): (i) the discrete lvs capture the socio-cultural norms of the dialogue turns, and (ii) the continuous lvs capture other aspects, e.g. related to fluency, topic, and meaning. these latent variables facilitate capturing label-and content-related properties of the previous turns of the conversation, and are conditioned on the previous turns in a markovian manner. we train the model on weakly annotated data using the variational technique, building on variational autoencoders (kingma and welling, 2014). to evaluate the performance of our model in the task of socio-cultural norm discovery, we conducted experiments on an existing dataset. experimental results show superiority of our model, by 4 points in f1 score, over the state-of-the-art approaches, in which each segment of a dialogue is modelled independently of the others. furthermore, by evaluating our model on low amounts of training data, we show the capability of our proposed approach in capturing socio-cultural norms on partially-labeled data. ## related works recent approaches have tried to develop models with the human psychological and behavioural capabilities @xcite @xcite . other approaches targeted identifying implicit social paradigms by developing sequence generation models @xcite . however, the task of socio-cultural norm discovery has been overlooked, mostly due to the lack of proper annotated data @xcite . @xcite present a dataset of social norms, collected from reddit and propose a generative model to expand this collection. @xcite also showed how social norms can be useful in conducting better negotiation dialogues. in a similar approach, @xcite present a corpus of moral norms. @xcite use a prompt-based large-scale language model to generate rules from dialogues. more similar to our approach, existing models identify labels associated with utterances of dialogues @xcite @xcite . however, these approaches fail to take into account the flow of contextual information throughout a dialogue. in contrast to these studies, our approach addresses this task by considering the inter-dependencies between turns of dialogues. ## a generative markov model for socio-cultural norm discovery we are given a set of dialogues @xmath0, where each dialogue consists of a set of turns (or segments) d @xmath1. each turn consists of a sequence of tokens from a vocabulary set v. the dialogue set d consists of two subsets of labeled (d l ) and unlabeled (d u ) dialogues, where each turn s i @xmath2. the turns in the unlabeled dataset lack socio-cultural norm labels. our goal is to develop a model that, by using contextual information carried from previous turns of the dialogue, discovers the socio-cultural norm associated with the turns of a dialogue. probabilistic generative model. our model (shown in fig. 2 ) assumes a directed generative model, in which a turn is generated by a factor capturing the socio-cultural norms and another factor capturing other aspects, e.g. topic and syntax. for each turn, the socio-cultural norm factor is captured by a discrete latent variable c i , and the other aspects are captured by a continuous latent variable z i . as our aim is to leverage the contextual information, the latent variables of each turn of the dialogue are conditioned on those from the previous turn in markovian manner. as such, our proposed generative model for each turn is as follows: training. to train the model, the likelihood function for a dialogue in d u is: intuitively, the training objective for each dialogue turn corresponds an extension of the variational autoencoder (vae) which involves: (i) both discrete and continuous latent variables, and (ii) conditioning on the latent variables of the previous turn. as such, we resort to the following variational evidence lowerbound (elbo) for the unlabeled turns: architectures. we use a transformer-based encoder to encode the turns s i with a hidden representation h s i . the classifier q ϕ (c i |s i , c i-1 ) is a 2-layer mlp with tanh non-linearity whose inputs are h s i and the embedding of c i-1 . for q ϕ (z i |s i , z i-1 ), we use a a multivariate gaussian distribution, whose parameters are produced by mlps from h s i and z i-1 . for p θ (s i |z i , c i ), we use an lstm decoder, where this is performed by replacing pre-defined special tokens in the embedding space with z i and c i . for p θ (c t |c t-1 ), we use mlp with a softmax on top. ## experiments in this section we report the performance of our model on the task of socio-cultural norm discovery in comparison to the current state-of-the-art models. ## conclusion in this work, we address the task of socio-cultural norm discovery from open-domain conversations. we present a probabilistic generative model that captures the contextual information from previous turns of dialogues. through empirical results, we show that our model outperforms state-of-the-art models in addressing this task.