S oc A o G : Incremental Graph Parsing for Social Relation Inference in Dialogues (2021)

Abstract: Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we formulate a sequential structure prediction task, and propose an \alpha - \beta - \gamma strategy to incrementally parse SocAoG for the dynamic inference upon any incoming utterance: (i) an \alpha process predicting attributes and relations conditioned on the semantics of dialogues, (ii) a \beta process updating the social relations based on related attributes, and (iii) a \gamma process updating individual’s attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference.

## introduction social relations form the basic structure of our society, defining not only our self-images but also our relationships @xcite . robots with a higher emotional quotient (eq) have the potential to understand users' social relations better and act appropriately. given a dialogue as context and a set of entities, the task of dialogue relation extraction (dre) predicts the relation types between the entities from a predefined relation set. existing researches using bert-based models @xcite @xcite or graph-based models @xcite s1: s1: why?! s3 : the sexy blonde behind the counter. s1: gunther?! s2: your mother just added him to her list. s1: what? your-your list? argument pair trigger relation type r1 (s2, s1) dad per:children r2 (s3, gunther) sexy blonde per:positive impression r3 (s3, s1) mom per:children r4 ( s1 , s3 ) mom per:parents r5 (s1, s2) dad per:parents chen et al., 2020) focus on identifying entities' relations from the semantics of dialogues-they utilize either the attention mechanism or a refined token graph to locate informative words (e.g., "dad" and "mom") that imply the argument pairs' relations. however, there are still three missing parts in current models for social relation inference according to our observations. first, current models lack the explicit modeling of the relational consistency among a group of people-such consistency helps humans reason about the social relation of two targets by using their relations with a third person. for the example in we evaluate our method on two datasets of di-alogre @xcite and moviegraph @xcite for relation inference, and the results show that our method outperforms the state-of-theart (sota) ones. overall, we make the following contributions: (i) we propose to model and infer social relations and individual's attributes jointly with socaog for the consistency of attributes and social relations among a group. to the best of our knowledge, it is the first time done in the dialogue do-main; (ii) the mcmc sampling from α-β-γ posterior enables dynamic inference-incrementally parsing the social relation graph, which can be useful for tracking relational evolution, reflecting the reasoning process, and handling long dialogues; (iii) we perform an ablation study on each process of α-β-γ to investigate the information contribution, and perform case studies to show the effectiveness of our dynamic reasoning. ## related work we review the related works on the social relation inference from documents, which is a well-studied task, and those from dialogues, which is the emerging task that our work is focused on. ## problem formulation our goal is to construct a social network through utterances in dialogue. the network is a heterogeneous physical system @xcite with particles representing entities and different types of edges representing social relations. each entity is associated with multiple types of attributes, while each type of relation is governed by a potential function defined in human attribute and value space, acting as the social norm. the relations are often asymmetric, e.g., a is b's father does not mean b is a's father. to model the network, we utilize an attributed and-or graph (a-aog), a probabilistic grammar model with attributes on nodes. such design takes advantage of the reconfigurability of its probabilistic context-free grammar to reflect the alternative attributes and relations, and the contextual relations defined on markov random field to model the social norm constraints. the social network graph, named socaog, is diagrammatically shown in figure 2 . formally, socaog is defined as a 5-tuple: jonah anna mother community society gender age ... profession kid senior adult woman dialog hey how are irene sam john colleague colleague man trans* father husband and-node and-node (attribute) word context terminal node (attribute) terminal node (character) social relation or-node (attribute subtype) jonah anna mother community society ... and-node and-node (attribute) word context terminal node (attribute) terminal node (character) social relation or-node (attribute subtype) given p to be the probability model defined on socaog, a parse graph pg is an instantiation of socaog with determined attribute selections for every or-node and relation types for every edge. for a dialogue session with t turns d @xmath0, d (2) , ..., d (t ) }, where d (t) is the utterance at turn t, our method infers the attributes and social relations incrementally over turns: ## conclusion the paper proposes a socaog model with α-βγ processes for the consistent inference of social relations in dialogues. the model can also leverage attribute information to assist the inference. mcmc is proposed to parse the relation graph incrementally, enabling the dynamic inference upon any incoming utterance. experiments show that our model outperforms state-of-the-art methods; case studies and ablation studies are provided for analysis. in the future, we will further explore how different initialization of the parse graph could help warm start the inference under various situations and how multi-modal cues could be leveraged.