Efficient Document-level Event Relation Extraction (2025)

Abstract: Event Relation Extraction (ERE) predicts temporal and causal relationships between events, playing a crucial role in constructing comprehensive event knowledge graphs. However, existing approaches based on pairwise comparisons often suffer from computational inefficiency, particularly at the document level, due to the quadratic operations required. Additionally, the predominance of unrelated events also leads to largely skewed data distributions. In this paper, we propose an innovative two-stage framework to tackle the challenges, consisting of a retriever to identify the related event pairs and a cross-encoder to classify the relationships between the retrieved pairs. Evaluations across representative benchmarks demonstrate our approach achieves better efficiency and significantly better performance. We also investigate leveraging event coreference chains for ERE and demonstrate their effectiveness.

## introduction event relation extraction (ere) aims at identifying relationships between events, especially temporal and causal connections. as illustrated in figure 1 , given the original text and three event mentions of interest, an ere model should detect and classify the temporal (e.g., overlaps and before) and causal (e.g., cause) relationships between them. ere plays a pivotal role in the construction of event knowledge graphs @xcite and supports a variety of tasks, such as future event prediction @xcite , machine reading comprehension @xcite , and multi-hop reasoning @xcite . ere is challenging due to the event relation variety and the required comprehension @xcite . for document-level ere (dere), the challenge intensifies, needing event disambiguation and connection across expansive narrative structures. previous research has mainly focused on enriching event semantics @xcite and @xcite , or exploiting large language models (llms) @xcite . nevertheless, current research faces a unique challenge in inefficient learning and inference because the determination of relationships requires pairwise classification after iterating through all event pairs @xcite , which inherently exhibits quadratic time complexity. additional training challenges arise due to the largely skewed data distribution, where most event pairs have no relation, becoming particularly critical for dere with a broader scope of events and lengthy sources @xcite . however, this aspect has been overlooked in existing studies, and we are the first to investigate the efficiency issue in dere with crucial yet unexplored temporal and causal relations. in this paper, we introduce a novel pruning-based two-stage paradigm for dere (figure 2 ). in the first stage of the framework, we employ a retriever model to efficiently sift through event mentions in latent embedding spaces and identify the related event pairs. afterwards, a cross-encoder is fine-tuned for event relation prediction on the narrowed set of candidate pairs. this approach effectively prunes the candidate event pairs to tackle inefficiency and deals with the skewed distribution to enhance performance. experimental results on event storyline corpus (esc, @xcite , richer event description (red, @xcite , and maven-ere @xcite demonstrate significantly better performance and efficiency compared to representative baselines. in summary, the key contributions of this paper are as follows: (1) we design a novel two-stage framework for dere by pruning candidate event pairs to reduce computational complexity and mitigate the skewed distribution issue. (2) we conduct rigorous evaluations and ablation studies on dere datasets with various retrievers and cross-encoders. ## related work recent progress of ere has been made based on pre-trained language models (plms), utilizing semantic structures @xcite , temporal clues @xcite , and external knowledge @xcite to enrich the event representations. some other works leverage the high-order transitivity @xcite and multi-task learning @xcite to model the dependencies between different relation types. some researchers further investigate the use of llms in ere @xcite @xcite . however, they often achieve this at the expense of computational efficiency when performing pairwise classifications, especially in document-level datasets @xcite . while some recent work focus on improving the efficiency of entity coreference resolution @xcite , they cannot be generalized to dere because of the requirement for deeper semantic analysis and the existence of more specific relation types. in this paper, we inherit the ideas of pruning but design a more effective framework with a retriever model and a cross-encoder model. ## methodology we formulate our dere task as a multi-class classification problem. formally, given a document d that contains multiple sentences and two event mentions e h and e t of interest, our goal is to predict the potential temporal (e.g., before) and causal (e.g., cause) relationships between them. following the framework shown in figure 2 , we introduce the implementation of the retriever and cross-encoder models for training and inference in detail. ## conclusion and future work we for the first time introduce a novel two-stage framework for dere, which improves both efficiency and model training. it first uses a retriever to identify event pairs, then a cross-encoder for event relation prediction. experimental results on three representative datasets underscore the effectiveness of our method, which significantly improves both accuracy and efficiency compared to the baseline models. we further investigate the efficacy of different encoding strategies, and demonstrate the effectiveness of leveraging coreference chains in candidate event pair identification. in the future, we will adapt our method to more ie tasks (e.g., entity relation extraction), and verify its generalizability.