Event-Event Relation Extraction using Probabilistic Box Embedding (2022)

Abstract: To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) framework regards it as a multi-class classification task and do not guarantee any coherence between different relation types, such as anti-symmetry. If a phone line “died” after “storm”, then it is obvious that the “storm” happened before the “died”. Current framework of event relation extraction do not guarantee this coherence and thus enforces it via constraint loss function (Wang et al., 2020). In this work, we propose to modify the underlying ERE model to guarantee coherence by representing each event as a box representation (BERE) without applying explicit constraints. From our experiments, BERE also shows stronger conjunctive constraint satisfaction while performing on par or better in F1 compared to previous models with constraint injection.

## introduction a piece of text can contain several events. in order to truly understand this text, it is vital to understand the subevent and temporal relationships between these events. @xcite @xcite . both temporal as well as subevent relationships between events satisfy transitivity constraints. for instance, in the paragraph, "there was a storm in atlanta in the night. all the phone lines were dead the next morning. i was not able to call for help.", the event marked by dead occurs after storm and the event call occurs after dead. hence, by transitivity, a sensible model should predict that storm occurs before call. in general, predicting the relationships between different events in the same document, such that these predictions are coherent, is a challenging task @xcite . while previous works utilizing neural methods provide competitive performances, these works employ multi-class classification per event-pair independently and are not capable of preserving logical constraints among relations, such as asymmetry and transitivity, during training time @xcite . to address this problem @xcite introduced a constrained learning framework, wherein they enforce logical coherence amongst the predicted event types through extra loss terms. however, since the coherence is enforced in a soft manner using extra loss terms, there is still room for incoherent predictions. in this work, we show that it is possible to induce coherence in a much stronger manner by representing each event using a box @xcite . we propose a box event relation extraction (bere) model that represents each event as a probabilistic box. box embeddings @xcite were first introduced to embed nodes of hierarchical graphs into euclidean space using hyperrectangles, which were later extended to jointly embed multi-relational graphs and perform logical queries @xcite . in this paper, we represent an event complex using boxes-one box for each event. such a model enforces logical constraints by design (see section 3.2). consider the example in figure 1 . event dead (e 2 ) follows event storm (e 1 ), indicating e 2 is child of e 1 . boxes can represent these two events as separate representations and by making e 1 to contain the box e 2 , which not only preserve their semantics, but also can infer its antisymmetric relation that event e 1 is a parent of event e 2 . however, the previous models based on pairwise-event vector representations have no real relation between representations (e 1 , e 2 ) and (e 2 , e 1 ) that can guarantee the logical coherence. experimental results over three datasets, hieve, matres, and event storyline (esl), show that our method improves the baseline @xcite ) by 6.8 and 4.2 f 1 points on single task and by 0.95 and 3.29 f 1 points on joint task over symmetrical dataset. furthermore, our bere model decreases conjunctive constraint violation rate by 85∼88% on a single-task models compared to plain vector model, and by 38% on joint-task model compared to constraint-injected vector model. we show that handling antisymmetric constraints, that exist among different relations, can satisfy the interwined conjunctive constraints and encourage the model towards a coherent output across temporal and subevent tasks. ## background task description given a document consisting of multiple events e 1 , e 2 , . . . , e n , we wish to predict the relationship between each event pair (e i , e j ). we denote by r(e i , e j ) the relation between event pair (e i , e j ). its values are defined in the label space {parent-child, child-parent, coref, norel} for subevent relationship (hieve) and {before, after, equal, vague} for temporal relationship (matres). 2 both subevent and temporal relationships have four similar-category relationship labels where the first two labels, (parent-child,child-parent) and (before, after) hold reciprocal relationship, the third label (coref and equal) occurs when it is hard to tell which of the first two labels that event pair should be classified to. lastly, the last label (norel and vague) represents a case when an event pair is not related at all. in the probabilistic gumbel box, these min and max points are taken to be independent gumbel-max and gumbel-min random variables, respectively. as shown in dasgupta et al. (2020), if b and c are two such gumbel boxes then their volume and intersection is given as: euler-mascheroni constant. 3 logical constraints we define symmetry and conjunction constraints of relations. symmetry constraints indicate the event pair with flipping orders will have the reversed relation. for example, if r(e i , e j ) = parent-child (before), then r(e j , e i ) = child-parent (after). given any two events, e i and e j , the symmetry consistency is defined as follows: ## experiments in this section, we describe datasets, baseline methods, and evaluation metrics. lastly, we provide experimental results and a detailed analysis of logical consistency. ## ablation study we conduct additional experiments to see whether vector trained with the augmented symmetrical dataset will affect the conclusion of bere-p. the results in ## conclusion we propose a novel event relation extraction method that utilizes box representation. the proposed method projects each event to a box representation which can model asymmetric relationships between entities. utilizing this box representation, we design our relation extraction model to handle antisymmetry between events of (e i , e j ) and (e j , e i ) which previous vector models were not capable of. through experiments on three datasets, we show that the proposed method not only free of antisymmetric constraint violations but also have drastically lower conjunctive constraint violations while maintaining similar or better performance in f 1 . our model shows that box representation can provide coherent classification across multiple event relations and opens up future research for box representations in event-to-event relation classification. 7 acknowledgement this work is based upon work supported in part by the center for data science and the center for intelligent information retrieval, and in part by the national science foundation under grant nos. 1763618 and 1514053, and in part by the chan zuckerberg initiative under the project "scientific knowledge base construction". some of the work reported here was performed using high performance computing equipment obtained under a grant from the collaborative r&d fund managed by the massachusetts technology collaborative. completion. in proceedings of the thirty-fourth annual conference on advances in neural information processing systems (neurips). jun araki, zhengzhong liu, eduard hovy, and teruko mitamura. 2014. detecting subevent structure for event coreference resolution. in proceedings of the ninth international conference on language resources and evaluation (lrec'14), reykjavik, iceland. european language resources association (elra). lukas biewald. 2020. experiment tracking with weights and biases. software available from wandb.com. tommaso caselli and piek vossen. 2017. the event storyline corpus: a new benchmark for causal and temporal relation extraction. in proceedings of the events and stories in the news workshop, pages 77-86, vancouver, canada. association for computational linguistics. nathanael chambers and daniel jurafsky. 2008. jointly combining implicit constraints improves temporal ordering. in proceedings of the 2008 conference on empirical methods in natural language processing, pages 698-706, honolulu, hawaii. association for computational linguistics.