Modeling Human Mental States with an Entity-based Narrative Graph (2021)

Abstract: Understanding narrative text requires capturing characters’ motivations, goals, and mental states. This paper proposes an Entity-based Narrative Graph (ENG) to model the internal- states of characters in a story. We explicitly model entities, their interactions and the context in which they appear, and learn rich representations for them. We experiment with different task-adaptive pre-training objectives, in-domain training, and symbolic inference to capture dependencies between different decisions in the output space. We evaluate our model on two narrative understanding tasks: predicting character mental states, and desire fulfillment, and conduct a qualitative analysis.

## introduction understanding narrative text requires modeling the motivations, goals and internal states of the characters described in it. these elements can help explain intentional behavior and capture causal connections between the characters' actions and their goals. while this is straightforward for humans, machine readers often struggle as a correct analysis relies on making long range common-sense inferences over the narrative text. providing the appropriate narrative representation for making such inferences is therefore a key component. in this paper, we suggest a novel narrative representation model and evaluate it on two narrative understanding tasks, analyzing the characters' mental states and motivations @xcite @xcite , and desire fulfillment @xcite . we follow the observation that narrative understanding requires an expressive representation capturing the context in which events appear and the interactions between characters' states. to clarify, consider the short story in fig. 1 . the desire expression appears early in the story and provides the context explaining the protagonist's actions. evaluating the fulfilment status of this expression, cindy really likes apples. she wanted to try something new with them. she decided to try to make baked apples for the first time. she gathered everything she needed and began cooking. it's now her favorite apple dish! desire expression: try something new with them motivation @xcite : curiosity emotion (plutchik): joy, anticipation desire fulfilled! motivation @xcite : independence emotion (plutchik): joy figure 1 : narrative example which tends to appear towards the end of the story, requires models that can reason over the desire expression ("trying something new"), its target ("apples") and the outcome of the protagonist's actions ("it's now her favorite apple dish!"). capturing the interaction between the motivation underlying the desire expression (in fig. 1 , curiosity) and the emotions (in fig. 1 , anticipation) likely to be invoked by the motivation can help ensure the consistency of this analysis and improve its quality. to meet this challenge, we suggest a graphcontextualized representation for entity states. similar to contextualized word representations @xcite , we suggest learning an entity-based representation which captures the narrative it is a part of. for example, in "she decided to try to make baked apples for the first time" the mental state of "she" would be represented differently given a different context, such as a different motivation for the action ("her mother asked her to make an apple dish for a dinner party"). in this case, the contextualized representation would capture the different emotion associated with it (e.g., fear of disappointing her mother). unlike contextualized word embeddings, entity-based contextualization needs to consider, at least, two levels of context: local text context and distant event context, which require more complicated modeling techniques to capture event semantics. moreover, the context of event relationships can spread over a long narrative, exceeding maximum sequence length limitation in modern contextualized word embedding models such as bert @xcite . in this paper, we propose an entity-based narrative graph (eng) representation of the text. unlike other graph-based narrative representations @xcite @xcite which require intensive human annotation, we design our models around low-cost supervision sources and shift the focus from symbolic graph representations of nuanced information to their learned embedding. in eng, each node is associated with an entityevent pair, representing an entity mention that is involved in an event. edges represent observed relations between entities or events. we adapt the definition of event relationships introduced in @xcite to our entity-event scenario. for entity relationships, the cnext relationship connects two coreferent entity nodes. for event relationships, the next relationship captures the sequential order of events as they appear in the text, and six discourse relation types from the penn discourse tree bank (pdtb) @xcite are used. these include before, after, sync., contrast, reason and result. note that these are extracted in a weakly supervised manner, without expensive human annotations. to contextualize the entity embeddings over eng, we apply a relational graph convolution network (r-gcn) @xcite , a relational variant of the graph convolution network architecture (gcn) @xcite . r-gcns create contextualized node representations by considering the graph structure through graph convolutions and learn a composition function. this architecture allows us to take into account the narrative structure and the different discourse relations connecting the entity-event nodes. to further enhance our model, we investigate three possible pre-training paradigms: whole-wordmasking, node prediction, and link prediction. all of them are constructed by automatically extracting noisy supervision and pre-training on a largescale corpus. we show that choosing the right pre-training strategy can lead to significant performance enhancements in downstream tasks. for example, automatically extracting sentiment for entities can impact downstream emotion predictions. finally, we explore the use of a symbolic inference layer to model relationships in the output space, and show that we can obtain additional gains in the downstream tasks that have strong correlation in the output space. the evaluated downstream tasks include two challenging narrative analysis tasks, predicting characters' psychological states @xcite and desire fulfilment @xcite . results show that our model can outperform competitive transformer-based representations of the narrative text, suggesting that explicitly modeling the relational structure of entities and events is beneficial. our code and trained models are publicly available foot_0 . ## related work tracking entities and modeling their properties has proven successful in a wide range of tasks, including language modeling @xcite , question answering @xcite ) and text generation @xcite . in an effort to model complex story dynamics in text, @xcite released a dataset for tracking the emotional reactions of characters in stories. in their dataset, each character mention is annotated with three types of mental state descriptors: maslow's "hierarchy of needs" @xcite , reiss' "basic motives" @xcite , that provide a more informative range of motivations, and plutchik's "wheel of emotions" @xcite , comprised of eight basic emotional dimensions (e.g. joy, sadness, etc). in their paper, they showed that neural models with explicit or latent entity representations achieve promising results on this task. @xcite approached this task by extracting multi-hop relational paths from conceptnet, while @xcite leveraged semantics of the emotional states by embedding their textual description and modeling the co-relation between different entity states. @xcite introduced a dataset for the task of desire fulfillment. they identified desire expressions in firstperson narratives and annotated their fulfillment status. they showed that models that capture the flow of the narrative perform well on this task. representing the narrative flow of stories using graph structures and multi-relational embeddings has been studied in the context of script learning @xcite @xcite . in these cases, the nodes represent predicatecentric events, and entity mentions are added as context to the events. in this paper, we use an entitycentric narrative graph, where nodes are defined by entity mentions and their textual context. we encode the textual information in the nodes using pre-trained language models @xcite , and the graph structure with a relational graph neural network @xcite . to learn the representation, we incorporate a task-adaptive pre-training phase. @xcite showed that further specializing large pre-trained language models to domains and tasks within those domains is effective. ## evaluation our evaluation includes two downstream tasks and a qualitative analysis. we report the results for different tapt schemes and symbolic inference on storycommonsense. for the qualitative analysis, we visualize and compare the contextualized graph embeddings and contextualized word embeddings. ## conclusions we propose an eng model that captures implicit information about the states of narrative entities using multi-relational graph contextualization. we study three types of weakly-supervised tapts for eng and their impact on the performance of downstream tasks, as well as symbolic inference capturing the interactions between predictions. our empirical evaluation was done over two narrative analysis tasks. the results show that eng can outperform other strong baselines, and the contribution of different types of tapt is task-dependent. in the future, we want to connect different tapt schemes and downstream tasks, and explore constrained representations.