Connecting the Dots: Event Graph Schema Induction with Path Language Modeling (2020)

Abstract: Event schemas can guide our understanding and ability to make predictions with respect to what might happen next. We propose a new Event Graph Schema, where two event types are connected through multiple paths involving entities that fill important roles in a coherent story. We then introduce Path Language Model, an auto-regressive language model trained on event-event paths, and select salient and coherent paths to probabilistically construct these graph schemas. We design two evaluation metrics, instance coverage and instance coherence, to evaluate the quality of graph schema induction, by checking when coherent event instances are covered by the schema graph. Intrinsic evaluations show that our approach is highly effective at inducing salient and coherent schemas. Extrinsic evaluations show the induced schema repository provides significant improvement to downstream end-to-end Information Extraction over a state-of-the-art joint neural extraction model, when used as additional global features to unfold instance graphs.

## introduction existing approaches to automated event extraction retain the overly simplistic assumption that events are atomic occurrences. understanding events requires knowledge in the form of a repository of abstracted event schemas (complex event templates). scripts @xcite encode frequently recurring event sequences, where events are ordered by temporal relation @xcite , causal relation @xcite , or narrative order @xcite . event schemas have become increasingly important for natural language understanding tasks such as story ending prediction @xcite and reading comprehension @xcite . previous schema induction methods mostly ignore uncertainty, re-occurring events and multiple hypotheses, with limited attention to capture complex relations among events, other than temporal or causal relations. temporal relations exist between almost all events, even those that are not semantically related; while research in identifying causal relations has been hobbled by low inter-annotator agreement @xcite . in this paper, we hypothesize that two events are connected when their entity arguments are coreferential or semantically related. for example, in figure 1 , (a) and (b) refer to very different event instances, but they both illustrate a typical scenario where a group of people moved from one place to another and then attacked the destination. from many such event instance pairs, we can induce multiple paths connecting a movement event to a related attack event: the person being moved became the attacker, and the weapon or vehicle being moved became the instrument of the attack. low-level primitive components of event schemas are abundant, and can be part of multiple, sparsely occurring, higher-level graph schemas. we thus propose a new schema representation, event graph schema, where two event types are connected by such paths containing entity-entity relations. each node represents an entity type or event type, and each edge represents an entity-entity relation type or the argument role of an entity played in an event. however, between two event types, there may also be noisy paths that should be excluded from graph schemas. we define the following criteria to select good paths in a graph schema: (1). salience: a good path should appear frequently between two event types; (2). coherence: multiple paths between the same pair of event types should tell a . . . transport fac gpe destination partwhole attack per located_in target graph schema induction attack transport fac a r t if a c t a tt a c k e r in st ru m en t wea loc or ig in d e s ti n a ti o n destination p a rtw h o le located_in gpe plac e per org target g e n e ra l a ffi lia ti o n att ac ke r partwhole ta r g e t part-whole artifact fac per troops attack attack deploy transport destination sevastopol ukraine gpe gpe russia agent origin part-whole instrument a r ti fa c t a ffi li a ti o n at ta ck er a rt if a c t target (a) attack hit per protesters per police gpe kyiv fac square maidan partwhole located_in target place wea stone gpe ukraine transport carry destination a tt a c k e r lo c a t e d _ in ar tif ac t instrument partwhole affiliation (b) partwhole wea tank figure 1: the framework of event graph schema induction. given a news article, we construct an instance graph for every two event instances from information extraction (ie) results. in this example, instance graph (a) tells the story about russia deploying troops to attack ukraine using tanks from russia; instance graph (b) is about ukrainian protesters hit police using stones that are being carried to maidan square. we learn a path language model to select salient and coherent paths between two event types and merge them into a graph schema. the graph schema between attack and transport is an example output containing the top 20% ranked paths. as the first attempt to extract such schemas, we propose a path language model to select paths which clearly indicate how two events are connected through their shared entity arguments or the entity-entity relations between their arguments. for example, in figure 1 (b) , maidan square and ukraine connect events transport and attack through the path transport we train the path language model on two tasks: learning an auto-regressive language model @xcite @xcite @xcite to predict an edge or a node, given previous edges and nodes in a path, and a neighboring path classification task to predict how likely two paths co-occur. the path language model is trained from all the paths between two event instances from the same document, based on the assumption that events from the same document (especially news document) tell a coherent story. we propose two intrinsic evaluation metrics, instance coverage and instance coherence, to assess when event instance graphs are covered by each graph schema, and when different schemas appear in the same document. intrinsic evaluation on heldout documents demonstrates that our approach can produce highly salient and coherent schemas. such event graph schemas can also be exploited to enhance the performance of information extraction (ie) tasks, such as entity extraction, relation extraction, event extraction and argument role labeling, because most of the existing methods ignore such inter-event dependencies. for example, from the following sentence "following the trail of mohammed a. salameh, the first suspect arrested in the bombing, investigators discovered a jumble of chemicals, chemistry implements and detonating materials...", the state-of-the-art ie system @xcite successfully extracts the arrestjail event but fails to extract the investigatecrime triggered by "discovered" and its defendant argument "mohammed a. salameh". event graph scehmas can inform the model that a person who is arrested was usually investigated, our ie system can fix this missing error. therefore we also conduct extrinsic evaluations and show the effectiveness of the induced schema repository in enhancing downstream end-to-end ie tasks. in summary, we make the following novel contributions: ## problem formulation given an input document, we extract instances of entities, relations, and events. the type set of entities and events is φ, and the type set of entity-entity relations and event argument roles is ψ. for every two event instances, we construct an event instance graph @xmath0, e, ϕ) ∈ g with all paths connecting the two, as in figure 1 (a) and (b). v and e are the node and edge sets, and ϕ : {v, e} → {φ, ψ} is a mapping function to obtain the type of each node or edge. each node v @xmath1, ϕ(v i ) ∈ v represents an entity or an event with text mention w i , and ϕ(v i ) ∈ φ denotes its node type. each set of coreferential entities or events is mapped to one single node. each edge e @xmath2, ϕ(e ij ), v @xmath3, where i and j denote the involved nodes. ## conclusions and future work we propose event graph schema induction as a new step towards semantic understanding of inter-event connections. we develope a path language model based method to construct graph schemas containing salient and semantically coherent eventevent paths, which also effectively enhances endto-end information extraction. in the future, we aim to extend graph schemas to encode hierarchical and temporal relations, as well as rich ontologies in open domain. we will also assemble our graph schemas to represent more complex scenarios involving multiple events, so they can be applied to more downstream applications including event graph completion and event prediction.