CHEER : Centrality-aware High-order Event Reasoning Network for Document-level Event Causality Identification (2023)

Abstract: Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail throughout the document, with most other events serving as either their cause or consequence. In this paper, we manually annotate central events for a systematical investigation and propose a novel DECI model, CHEER, which performs high-order reasoning while considering event centrality. First, we summarize a general GNN-based DECI model and provide a unified view for better understanding. Second, we design an Event Interaction Graph (EIG) involving the interactions among events (e.g., coreference) and event pairs, e.g., causal transitivity, cause(A, B) AND cause(B, C) → cause(A, C). Finally, we incorporate event centrality information into the EIG reasoning network via well-designed features and multi-task learning. We have conducted extensive experiments on two benchmark datasets. The results present great improvements (5.9% F1 gains on average) and demonstrate the effectiveness of each main component.

## introduction event causality identification (eci) aims at identifying causal relations between events within texts. it is a fundamental nlp task and beneficial to various applications, such as question answering @xcite and future event forecasting @xcite . in terms of the text length, events may occur within the same sentence (seci) or span across the entire document (deci). deci is more practical than seci but suffers from the lack of clear causal indicators, e.g., causal words because. recent deci works often build a documentlevel graph for cross-sentence reasoning, but ignore important causal structures. tran phu and nguyen a large fire broke out at the waitrose supermarket in wellington's high street. half of the roof at the entrance of the store collapsed during the blaze. it will take a couple of days for repairs. a man has been charged with arson after an investigation over the fire. coreference causal relation figure 1 : an example of deci. solid green lines denote target causal relations and dashed yellow lines denote coreference. fire is the central event in this document. in this paper, we propose to consider the above causal structures while leveraging the reasoning power of gnn. to do so, we highlight the following questions: to address the issues, we manually annotate central events in the public dataset eventstory-line @xcite and propose a novel deci model, centrality-aware high-order event reasoning network (cheer). we first summarize a general gnn-based deci model for better understanding. then, we design an event interaction graph (eig) that involves interactions between events and among event pairs (i.e., highorder relations). finally, we incorporate event centrality information into the eig reasoning network via well-designed features and multi-task learning. in specific, for the first challenge, we preserve centrality information into event embeddings using two measures: (i) position centrality to maintaining the order of sentences where events are located, and (ii) degree centrality that counts the number of prior relations of each event. the motivation is that a central event usually summarizes the main content at the beginning and almost all the other events are relevant to it. then, we use the centrality-aware event embeddings for central event prediction. evaluated on our central event annotations, we found that this centrality modeling method is feasible and effective, with potential for further improvement. for the second challenge, based on the general gnn-based deci model, our proposed eig unifies both event and event-pair graphs, so that we can reason over not only available causal structures but also high-order event relations. particularly, there are three types of edges. first, two event pair nodes shall be connected if they share a common event, so that their relational information can be fused for transitivity. second, we connect event nodes to their corresponding event pair nodes to enhance event embeddings with high-order reasoning. moreover, the edge types will be further distinguished according to whether the event node is a central event or not. third, eig is also scalable to prior event relations (e.g., coreference) that connect event nodes if available. our contributions can be summarized as follows: ## methodology given document d and all its events, deci is to predict whether there is a causal relation between any two event mentions e i and e j in d. as shown in figure 2 , our proposed cheer includes four main components: (1) document encoder to encode the document and output contextualized representations of events; (2) event interaction graph that builds a graph including event nodes and event pair nodes for document-level reasoning. (3) event centrality incorporation that incorporates event centrality information through two aspects. (4) eig reasoning network that improves the quality of event and event pair representations by conducting inference over eig, and then combines two types of node embeddings for final classification. ## conclusion in this paper, we propose a novel centrality-aware high-order event reasoning network (cheer) to conduct global reasoning for deci. we first summarize a general gnn-based deci model and provide a unified view for better understanding. then we design an event interaction graph (eig) that involves prior event relations and high-order interactions among event pairs. finally, we incorpo-rate event centrality via well-designed features and multi-task learning. extensive experiments show a great improvement of cheer for both intra-and inter-sentence eci on two benchmark datasets. further analysis demonstrates the effectiveness of each main component.