Event Causality Extraction with Event Argument Correlations (2022)

Abstract: Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding. However, the ECI task ignores crucial event structure and cause-effect causality component information, making it struggle for downstream applications. In this paper, we introduce a novel task, namely Event Causality Extraction (ECE), aiming to extract the cause-effect event causality pairs with their structured event information from plain texts. The ECE task is more challenging since each event can contain multiple event arguments, posing fine-grained correlations between events to decide the cause-effect event pair. Hence, we propose a method with a dual grid tagging scheme to capture the intra- and inter-event argument correlations for ECE. Further, we devise a event type-enhanced model architecture to realize the dual grid tagging scheme. Experiments demonstrate the effectiveness of our method, and extensive analyses point out several future directions for ECE.

## introduction event causality @xcite denotes an explicit causal relation between two events, constituting a specific cause-effect event pair. as shown in figure 1 , a causal relation exists between a price rise event (the worldwide rise of oil prices) and a cost rise event (increases the cost of international shipping industry). understanding such event causality could facilitate various downstream applications including event forecasting @xcite , intelligent search @xcite ) and question answering @xcite , which is important for natural language understanding. in recent years, it has aroused the research interest for event causality identification (eci) @xcite @xcite no. event type event roles product region industry 2020 @xcite , which aims to detect whether the causality exists between two given events. despite of its success, there exist two issues that the eci task fails to address. 1) event structure missing, where each event in eci is only expressed using a word or phrase which reflects its occurrence, but ignores the explicit event type and event arguments (i.e., entities which participate in the event). the absence of such event structure would lose valuable clues for understanding event causality. as shown in figure 1 , "oil" plays a product role in a price rise-typed cause event, implying a consequent cost risetyped effect event towards "shipping industry". 2) causality component missing, where eci only predicts the existence of causality between the given event pairs, ignoring to discriminate the specific cause/effect event causality component. limited by these issues, eci insufficiently explores the causality between events, which demands further promotion to the understanding of event causality. motivated by discussion about event causality in ccks (2021), we therefore formulate a task termed as event causality extraction (ece). as figure 1 shows, ece aims to end-to-end extract the cause-effect event pairs with structured event information from plain texts. comparing with eci, ece illustrates the event causality including the event structure, namely event types and arguments, and the specific cause-effect causality component, making it more informative to support the various downstream applications @xcite . intuitively, ece could be achieved by successively extracting the structured event and then classifying their causality relation. unfortunately, such a paradigm would easily suffer from the redundant event-pair problem, where the causality-unrelated events would be inevitably extracted, confusing the causality decision. another promising direction is to borrow ideas from relational triple extraction (rte), which shares the similar task formulation. however, comparing with the entitycentric rte task, the event-centric ece raises new challenges: 1) intra-event argument correlations. specifically, ece focuses on event, which is a structure maintaining interactive correlations among its arguments. for example in figure 1 , the argument "new energy" and "ammonia fuel" in the demand rise event have strong semantic correlation. while rte focuses on individual entity, thus simply adapting rte models cannot capture such correlations to derive the event structure. 2) inter-event argument correlations. concretely, the event arguments involved in a cause/effect event pair usually show semantic correlations for causality deduction. as shown in figure 1 , event pricing_rise which occurs in "worldwide" region could imply event cost_rise in "international" region. it demonstrates that the interevent argument correlations not only provide important clues to decide causality, but also benefit reliable cause/effect event extraction with mutual confirmation between the cause-effect pair. in this paper, we propose an effective approach named dualcor, which explores both the intraevent and inter-event argument correlations with a dual grid tagging scheme for ece. specifically, dualcor contains two grid tagging tables regarding event types and the input sentence, to respectively derive the event structures for cause and effect events. in each table, dualcor extracts structured event arguments according to different event types, naturally considering intra-event argument correlations. further, when predicting the event arguments in the cause/effect table, dualcor also predicts their corresponding effect/cause event arguments, serving as auxiliary arguments to promote inter-event argument correlations. by confirming the auxiliary arguments in the other table, dualcor matches reliable cause-effect event pairs as predictions. to realize the above dual grid tagging scheme, we further devise a type-aware encoder, which refines textual representations with essential event type information to enhance argument prediction. we conduct the dual grid tagging on the type-aware textual representations to derive the final cause-effect event pair. overall, our main contributions include: ## related works this paper explores a novel ece task, which aims to extract the cause-effect event pairs with structured event information from plain texts. existing event-causality-related researches mostly focus on event causality identification, which predicts the causality for the previously given event pairs. they can be roughly categorized into three groups: (1) early works exploit the linguistic features @xcite , causal patterns @xcite and statistical causal associations @xcite to explore the causality between events. (2) recent researchers @xcite @xcite ) pay the major focus on incorporating external knowledge for causality identification with limited training data. (3) different from works above which conduct eci within a single sentence, tran phu and nguyen (2021) focus on document-level eci, where the given events scatter in multiple sentences. despite of their success, they all suffer from the issues of event structure missing and causality component missing. to our best knowledge, ece is the first to simultaneously derive the structured event information and explicit causality component, which could better support the downstream applications. other than ece, relational triple extraction (rte) @xcite has the similar task formulation, the ideas of which could actually be adapted for ece. concretely, rte detects entity pairs in a sentence and predicts pre-defined relation types between them. existing approaches for rte could be roughly categorized into two lines. (1) traditional joint methods, which solve rte through sequential interrelated steps via task decomposition @xcite @xcite or sequence generation @xcite . unfortunately, these methods all suffer from the exposure bias @xcite problem due to the gap from training to inference between multiple steps. (2) unified joint methods, which simultaneously derive the triplet entities and relations in onestage without cascading between steps, and is thus free from the exposure bias. these methods solve rte in either a sequence-labeling manner @xcite or grid-filling manner @xcite . however, the entity-centric rte methods seem to struggle for the event-centric task, since events present more complicated argument correlations either intra-and inter events. ## task formulation event causality extraction (ece) aims to derive the cause-effect event pairs from plain texts. here, a cause-effect event pair contains a cause component and an effect component, where each component denotes an event with a specific event type and its event arguments with their event roles. given a piece of text, an event causality extraction system is required to predict all the cause-effect event pairs from it as figure 1 shows. ## dual grid tagging scheme this section introduces our proposed dual grid tagging scheme for the ece, including the tagging scheme and its decoding strategy. the specific model implementation is introduced in section. 5. ## model in this section, we introduce the model architecture to implement dualcor as figure 3 shows. ## conclusion in this paper, we formulate a new task, event causality extraction (ece), which aims to extract the cause-effect event pairs with structured event information from plain texts. we propose a method based on an elaborately devised dual grid tagging scheme, which explores the intra-and inter-event argument correlations for the task. experiment results prove the effectiveness of our method, and extensive analyses are conducted to point out several promising directions to inspire future works.