Enhancing Event Causality Identification with Event Causal Label and Event Pair Interaction Graph (2023)

Abstract: Most existing event causality identification (ECI) methods rarely consider the event causal label information and the interaction information between event pairs. In this paper, we propose a framework to enrich the representation of event pairs by introducing the event causal label information and the event pair interaction information. In particular, 1) we design an event-causal-label-aware module to model the event causal label information, in which we design the event causal label prediction task as an auxiliary task of ECI, aiming to predict which events are involved in the causal relationship (we call them causality-related events) by mining the dependencies between events. 2) We further design an event pair interaction graph module to model the interaction information between event pairs, in which we construct the interaction graph with event pairs as nodes and leverage graph attention mechanism to model the degree of dependency between event pairs. The experimental results show that our approach outperforms previous state-of-the-art methods on two benchmark datasets EventStoryLine and Causal-TimeBank.

## introduction event causality identification (eci) aims to identify the causal relationship between pairs of events in the text. as shown in figure 1(a) , giving text and events as inputs, the eci model needs to identify three causal @xmath0, cause, e5 >, < e5, cause, e2 >, @xmath1, cause, e2 >. as a semantic relationship, causality is important for semantic understanding and discourse analysis. moreover, causal knowledge identified from a text can be useful for many natural language processing tasks @xcite @xcite . various approaches have been proposed for eci, from the early feature-based methods @xcite *corresponding author. meanwhile, isps who use ( e 1 ) bandwidth on the seacom cable have been scrambling(e2) to implement(e3) contingency(e4) plans(e5) to keep(e6) their customers connected(e7). the event pairs composed of seven events in (a), where the dark area denotes the event pairs composed of causality-related events. @xcite to the current neural-network-based methods @xcite @xcite @xcite . the existing methods have achieved impressive performance. however, as far as we know, they usually improve the performance of eci models by introducing commonsense knowledge or generating additional training data through data augmentation, with less focus on the inherent characteristics of the data. specifically, 1) minimal use of event causal label information. in the eci task, a sentence usually contains multiple events. however, not all events are involved in causality. for instance, the sentence shown in figure 1 (a) has seven events, among which e2, e5, and e7 are causality-related events, whereas e1, e3, e4, and e6 are causality-unrelated events. in general, there is a strong semantic correlation between events that have a causal relationship. if the model can first identify e2, e5, and e7 as causality-related events according to the dependencies between events and contextual semantics, it can limit the identification scope of causality to some extent, as shown in figure 1(b), thereby reducing the interference of unrelated events. furthermore, by performing statistical analysis on two benchmark datasets, eventstory-line and causal-timebank, we find that 55.13% and 38.63% of the sentences with events in the two datassets contained four or more events, where the average number of the different candidate event pairs is 13.12 and 10.64, and the average number of the event pairs with causal relationship is 1.99 and 0.30, respectively. if we can first predict which events are causality-related events according to the dependencies between them, the average number of candidate event pairs composed of causality-related events is 3.68 and 0.37, respectively, thereby narrowing the range of causality from 13.12 and 10.64 to 3.68 and 0.37, respectively. therefore, it is necessary to introduce the event causal label information to assist the eci tasks. 2) little focus on the interaction between event pairs. the existing method (phu and nguyen, 2021) uses events as nodes and leverages toolkits to obtain information (such as dependency parsing tree and coreference) to construct event graph. however, they are modeled with events as nodes, cannot directly learn the interaction between event pairs. as shown in figure 1 (a), e2 and e7 are far from each other in the text. thus, directly identifying the causal relationship between them is difficult for the model. @xmath2, cause, @xmath3, cause, @xmath4, it is easier to infer the causal relationship between e2 and e7 according to the transitivity of the causal relationship @xcite . therefore, it is essential to introduce the interaction information between event pairs to learn their dependencies. to address the above limitations, we propose a framework called eclep, which introduces event causal label information and event pair interaction information to enrich the representation of event pairs. in particular, 1) we design an eventcausal-label-aware module to model the event causal label information, in which we introduce the event causal label prediction task as an auxiliary task of eci to mine the dependencies between events. 2) we further design an event pair interaction graph module to model the interaction information between event pairs. in particular, we construct the interaction graph with event pairs as nodes and adopt graph attention mechanism to model the degree of dependency between event pairs. the experimental results on two benchmark datasets show that our overall framework outperforms previous state-of-the-art methods. ## methodology in eci, the goal is to predict whether a causal relationship exists in each event pair (e i , e j )(i ̸ = j) by giving text @xmath5, w 2 , ..., w n ) and events set @xmath6, e 2 , ..., e m ) contained in s as inputs, where w i denotes the i-th word in s, and e i denotes the i-th event in e. the overall framework of the proposed method is shown in figure 2 , which mainly includes four modules, we illustrate each component in detail in the following. ## conclusion in this paper, we propose a framework to enrich the representation of event pairs by introducing event causal label information and event pair interaction information. the experimental results on two widely used datasets indicate that our approach is effective for the eci task. in the future, we aim to mine other potential causal features for this task and apply our model to other types of relation extraction tasks, such as temporal relation extraction.