Identifying while Learning for Document Event Causality Identification (2024)

Abstract: Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document. Existing studies adopt a kind of *identifying after learning* paradigm, where events’ representations are first learned and then used for the identification. Furthermore, they mainly focus on the causality existence, but ignoring causal direction. In this paper, we take care of the causal direction and propose a new *identifying while learning* mode for the ECI task. We argue that a few causal relations can be easily identified with high confidence, and the directionality and structure of these identified causalities can be utilized to update events’ representations for boosting next round of causality identification. To this end, this paper designs an *iterative learning and identifying framework*: In each iteration, we construct an event causality graph, on which events’ causal structure representations are updated for boosting causal identification. Experiments on two public datasets show that our approach outperforms the state-of-the-art algorithms in both evaluations for causality existence identification and direction identification.

## introduction event causality identification (eci) is the task of identifying whether there exists a causal relation between two events. eci can facilitate a wide range of practical applications, including knowledge graph construction @xcite , question answering @xcite , and information extraction @xcite . the eci task can be divided into the sentence-level eci (two events are in the same sentence) and document-level eci (two events may be in different sentences). in this paper, we focus on the document-level eci task, which faces greater challenges due to the requirement of comprehending long texts for crosssentence reasoning. the traditional feature-based methods @xcite utilize integer linear programming (ilp) to model the document causal structure. in order to better capture the interactions among events, recent methods @xcite @xcite usually construct document-level undirected graphs to facilitate cross-sentence causal reasoning. other methods @xcite use sparse attention to address the issue of long-distance dependencies and distinguish between intra-and inter-sentential reasoning. modeling the interactions among events has been proven effective for the document-level eci task, however, almost all existing methods focus on only identifying the existence of causal relation between the event e i and e j , yet without considering the causality direction being from e i to e j (or from e j to e i ). in this paper, e @xmath1. this may lead to the learning of events' representations towards capturing events' correlations, but correlations may not be directly mapped into causalities @xcite . furthermore, undirected connections may also lead to incorrect causality identifications, as some properties of causal structures cannot be respected without directionality. there are three basic causal structures, namely, the chain, fork, and collider @xcite . causality identification without directionality cannot well exploit causal structures. as shown in figure 1 , "shooting e1 "-→"killing e4 "-→"arrested e5 " is a chain causal structure. for a model considering causality direction, if the two directional causal relations, i.e., e 1 → e 4 and e 4 → e 5 , can be first identified with high confidence, then this can help to identify the causal relation between e 1 and e 5 due to the causal transmission in the chain structure. we argue that events' causal relation should be with directionality, and considering causal directions could further boost event causality identification. besides ignoring directionality, existing solutions for the eci task adopt a kind of identifying after learning paradigm. that is, learning events' representations first via some advanced neural networks, and then identifying causal relations for all event pairs at only one pass. however, it could happen that some causal relations can be easily identified with high confidence. as reported by @xcite , identifying intra-sentence events' causality (two events in a same sentence) is often easier and with better accuracy than identifying inter-sentence events' causality (two events in different sentences). this motivates us to propose a new identifying while learning mode for the eci task. that is, identifying some events' causal relations with high confidence, and then utilizing the directionality and structure of such identified causalities to update events' representations for boosting next round of causality identification. motivated from the aforementioned considerations, this paper proposes an iterative learning and identifying framework (ilif) for the documentlevel event causality identification. for an event e i , we not only encode its contextual text representation h i , but also update its causal structure representation z i in each iteration. causality identification is modeled as a classification issue based on the representation h i and z i of an event pair. initially, we employ a pretrained language model to encode h i . in each iteration, we first construct a directed event causality graph (ecg) based on the identified causalities, and propose a causal graph encoder to next update z i on the ecg. after the termination, we output the directed ecg as the final causality identification results. in order to differentiate the importance of iterations, we design a novel iteration discounted loss function to mitigate the error propagation issue. we conduct experiments on two public datasets: the eventstoryline(v0.9) dataset @xcite and maven-ere dataset @xcite and consider both direction and existence settings for causal relations. we preprocess the eventstoryline dataset foot_0 to ensure that each ground truth ecg is a directed acyclic graph @xcite . experiment results validate that our ilif outperforms the state-of-the-art competitors for the document-level eci task in evaluations for both causality existence identification and direction identification. document-level eci compared to the sentencelevel eci, document-level eci holds greater potential for applications but also faces greater challenges, such as weak long-term dependencies and a lack of clear causal indicators. to address these challenges, researchers attempt to construct the topological structure of events for global reasoning. @xcite propose an approach based on integer linear programming (ilp) to model the initialize eci eci eci cge cge causal graph encoder (cge) intra path inter path intra aggregating • • • e 1 t1 inter aggregating e 7 e 6 e 4 • • • e 4 e 5 β ... ... identifying while learning w11 t1 w34 t1 w34 t1 w44 t1 w44 t1 e 3 e 3 e 2 e 2 e 4 t1 e 4 t1 w24 t1 w61 t2 w41 t2 w71 t2 w54 t2 t2 e 1 t2 e 1 t2 1-β e6 e5 e3 e4 e2 e7 e8 e1 cgc cge contextual text representation h 1 h 2 h 3 h 8 • • • contextual text representation h 1 h 2 h 3 h 8 • • • ... ... contextual text encoder (cte) ... ... contextual text encoder (cte) input document: paris store hit by @xmath02.6 million ( two million euros ) in jewelry, according to afp. a police source reported that the men drove e5 a large four-byfour car into the store in the early hours of the morning, grabbing e6 jewelry and luxury watches before setting firee7 to the vehicle and escaping e8 in another car. l iterations l iterations cgc cgc causal graph representation z 1 z 2 z 3 z 4 z 8 • • • z 7 • • • final ecg eci e 3 e 3 e 2 e 2 e 7 e 7 e 4 e 4 z2 z3 z3 figure 2 : illustration of the iterative learning and identifying framework (ilif). causality identification is based on events' contextual text and causal graph representations. the event causality graph (ecg) is iteratively constructed to update events' causal graph representations. the final ecg contains all identified causal relations as the output. to the best of our knowledge, existing methods ignore casuality direction and adopt the identifying after learning paradigm for the eci task. in this paper, we take care of causality direction and propose an identifying while learning mode. ## methodology we propose an iterative learning and identifying framework (ilif) for document-level event causality identification. the basic idea is to iteratively update events' representations for causality identification by exploiting causal structures on the most recent event causality graph. as shown in figure 2 , the ilif includes four main module: (1 ## conclusion in this paper, we propose a novel identifying while learning mode where the central idea is to iteratively update events' representations for boosting next round of causality identification. within the proposed iterative learning and identifying framework, an event causality graph is constructed in each iteration based on previously identified causal relations with high confidence, which helps to mine events' directed interactions for updating their representations. experiments on two widely used datasets have validated the superiority of this new working mode.