Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network (2024)

Abstract: Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses. To address these issues, we propose SemDI - a simple and effective Sem antic D ependency I nquiry Network for ECI. SemDI captures semantic dependencies within the context using a unified encoder. Then, it utilizes a Cloze Analyzer to generate a fill-in token based on comprehensive context understanding. Finally, this fill-in token is used to inquire about the causal relation between two events. Extensive experiments demonstrate the effectiveness of SemDI, surpassing state-of-the-art methods on three widely used benchmarks. Code is available at https://github.com/hrlics/SemDI .

## introduction event causality identification (eci) aims to catch causal relations between event pairs in text. this task is critical for natural language understanding (nlu) and exhibits various application values. for example, an accurate eci system can facilitate question answering @xcite , narrative generation @xcite , and summarization @xcite . however, identifying causal relationships within text is challenging due to the intricate and often implicit causal clues embedded in the context. for instance, in the sentence "but tremors are likely in the junk-bond market, which has helped to finance the takeover boom of recent years.", an eci model should identify the causal relation between event pair (tremors, boom), which is not immediately evident without understanding the context. * corresponding author (lihuang@swufe.edu.cn). the conventional approach for eci involves a binary classification model that takes a triplet (sentence, event-1, event-2) as input to determine the existence of a causal relation between the two events, as illustrated at the top of figure 1 . various methods have been proposed to enhance eci performance. while early feature-based methods @xcite @xcite laid the foundation, more recent representation-based methods have demonstrated superior eci capabilities, including pre-trained language models (plms) based methods @xcite , and data augmentation methods @xcite . a notable recent trend is augmenting eci models with external prior knowledge @xcite @xcite . however, it can also introduce potential bias. for example, consider the event pairs (winds, blackout) mentioned in figure 1. while there seems to be no direct causal re-lation from prior knowledge, contextual inference makes it reasonable to deduce causality. upon analysis, we can observe a causal semantic dependency between "winds" and "blackout": winds knocked down -------→ power lines causing ----→ blackout. this reveals that causal relations between events within a sentence often appear as context-dependent semantic dependencies. thus, we claim that the eci task can be reformulated as a semantic dependency inquiry task between two events within the context. to this end, we propose a heuristic semantic dependency inquiry network (semdi) for the eci task. the key idea behind semdi is to explore implicit causal relationships guided by contextual semantic dependency analysis. specifically, we first capture the semantic dependencies using a unified encoder. then, we randomly mask out one event from the event pair and utilize a cloze analyzer to generate a fill-in token based on comprehensive context understanding. finally, this fill-in token is used to inquire about the causal relation between the two events in the given sentence. the main contributions of this work are summarized as follows: ## related work identifying causal relationships between events in the text is challenging and has attracted massive attention in the past few years @xcite . early approaches primarily rely on explicit causal patterns @xcite , lexical and syntactic features @xcite , and causal indicators or signals @xcite to identify causality. recently, representation-based methods leveraging pre-trained language models (plms) have significantly enhanced the eci performance. to mitigate the issue of limited training data for eci, @xcite proposed data augmentation methods that generate additional training data, thereby reducing overfitting. recognizing the importance of commonsense causal relations for eci, @xcite ; @xcite ; @xcite incorporated external knowledge from the knowledge graph conceptnet @xcite to enrich the representations derived from plms. however, the effectiveness of external knowledgebased methods is highly contingent on the consistency between the target task domain and the utilized knowledge bases, which can introduce bias and create vulnerabilities in these approaches. in contrast to previous methods, man et al. (2022) introduced a dependency path generation approach for eci, explicitly enhancing the causal reasoning process. @xcite exploited two types of semantic structures, namely event-centered structure and event-associated structure, to capture associations between event pairs. ## experiments in this section, we empirically investigate the effectiveness of semdi, aiming to answer the following questions: (1) can semdi consistently perform well across various eci benchmarks? (2) can the proposed moduls (e.g., cloze analyzer) effectively enhance performance? (3) does semdi exhibit interpretability during the causality inquiry process? (4) is semdi robust to diffrent backbone sizes and masking strategies? ## conclusions in this paper, we present semdi, a simple and effective semantic dependency inquiry approach for event causality identification. we first encode the semantic dependencies using a unified encoder. subsequently, we utilize a cloze analyzer to generate a fill-in token based on comprehensive context understanding. this token is then used to inquire about the causal relation between two events within the context. extensive experiments on three widely recognized datasets demonstrate the superior performance of semdi while highlighting its robustness and efficiency.