Semantic Structure Enhanced Event Causality Identification (2023)

Abstract: Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually expressed by implicit associations between events. Existing methods usually capture such associations by directly modeling the texts with pre-trained language models, which underestimate two kinds of semantic structures vital to the ECI task, namely, event-centric structure and event-associated structure. The former includes important semantic elements related to the events to describe them more precisely, while the latter contains semantic paths between two events to provide possible supports for ECI. In this paper, we study the implicit associations between events by modeling the above explicit semantic structures, and propose a Semantic Structure Integration model (SemSIn).It utilizes a GNN-based event aggregator to integrate the event-centric structure information, and employs an LSTM-based path aggregator to capture the event-associated structure information between two events. Experimental results on three widely used datasets show that SemSIn achieves significant improvements over baseline methods.

## introduction event causality identification (eci) is an important task in natural language processing that seeks to predict causal relations between events in texts. as shown in the top of figure 1 , given the unstructured text and event pair (shot, protect), an eci model needs to identify that there exists a causal relation between two events, i.e., protect cause -→ shot. eci is an important way to construct wide causal connections among events, which supports a variety of practical applications, such as event prediction @xcite , reading comprehension @xcite , and question answering @xcite . eci is challenging because most causal relations are expressed by texts implicitly, which requires the model to understand the associations between two events adequately. existing methods directly model the texts with the pre-trained language model (plm) @xcite @xcite . they mainly rely on the ability of plms, which cannot capture associations between events comprehensively. to enrich the associations between events, some methods @xcite introduce external knowledge, such as events in conceptnet @xcite that are related to focused events. other methods @xcite utilize the data augment framework to generate more training data for the model. however, the above methods are far from fully modeling the associations among events in the texts. actually, texts contain rich semantic elements and their associations, which form graph-like semantic structures, i.e., semantic graphs. figure 1 shows the semantic graph generated by the abstract meaning representation (amr) @xcite parser for the corresponding text, where the nodes indicate events, entities, concepts, and other semantic elements, while edges with semantic roles describe the associations among semantic elements. for example, "protect-01" indicates the specific sense of the verb "protect" in the propbank @xcite 1 . "arg0", "arg1" and "arg2" indicate different semantic roles. in this semantic graph, we exploit two kinds of structures vital to the eci task, namely, event-centric structure and eventassociated structure. as shown in the bottom left of figure 1 , the event-centric structure consists of events and their neighbors, which describes events more precisely by considering their arguments and corresponding roles. for example, besides event "protect-01", "person (horton)" and "person (student)" are also important semantic elements, and their corresponding semantic roles can supply the extra information for the event. as shown in the bottom right of figure 1 , the event-associated structure contains semantic paths between two events, and each path contains the core semantic elements. the composition of these elements indicates the possible semantic relations between events and provides supports for eci. for example, the path "protect-01 :arg0 ----→ person :arg1 -1 -----→ shoot-02" indicates that "person (horton)" protects somebody first and then was shot. events "protect-01" and "shoot-02" share the same participant, and there may exist a causal relation between them. to make use of the above semantic structures in texts to carry out the eci task, we propose a new semantic structure integration model (sem-sin). it first employs an amr parser to convert each unstructured text into a semantic graph and obtains the above two kinds of semantic structures in that graph. for the event-centric structure, sem-sin adopts an event aggregator based on graph neural networks (gnn). it aggregates the information of the neighboring nodes to the event nodes to obtain more precise representations of the events. for the event-associated structure, semsin utilizes a path aggregator based on long short-term memory (lstm) network. it encodes the compositional semantic information in the paths and then integrates the information of multiple paths with an attention mechanism. with the above representations of the events and paths as input, semsin conducts 1 a corpus annotated with verbs and their semantic roles. ## related work identifying causal relations between events has attracted extensive attention in the past few years. early methods mainly rely on the causal association rule @xcite and causal patterns @xcite @xcite . some following methods exploit lexical and syntactic features to improve performance @xcite . recently, most of works apply plm to conduct eci @xcite @xcite . although plm has a strong ability for capturing the associations among tokens in the texts, they are not capable of this task because the associations between events are implicit. to enhance plm, recent works try to introduce external knowledge. @xcite proposed a method to enrich the representations of events using commonsense knowledge related to events from the knowledge graph conceptnet @xcite . @xcite further proposed a model to exploit knowledge connecting events in the conceptnet for reasoning. @xcite proposed a data augmented method to generate more training samples. instead of introducing external knowledge to enhance the abilities of the eci model, we attempt to dive deep into the useful semantic structure information in the texts. ## the semsin model in this section, we introduce the proposed semsin model. figure 2 illustrates the overall architecture of the semsin model. given an input text, semsin first uses a pre-trained amr parser to obtain the corresponding semantic graph of the text. then, the event-centric structure and the event-associated structure constructed from the semantic graph, as well as the original text, are fed into the following three components respectively: (1) event aggregator aggregates the event-centric structure information into the representation of the event pair. ## conclusions in this paper, we proposed a new semantic structure integration model (semsin) for eci, which leveraged two kinds of semantic structures, i.e., eventcentric structure and event-associated structure. an event aggregator was utilized to aggregate eventcentric structure information and a path aggregator was proposed to capture event-associated structure information between two events. experimental results on three widely used datasets demonstrate that introducing semantic structure information helps improve the performance of the eci task.