Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks (2021)

Abstract: Identifying causal relations of events is an important task in natural language processing area. However, the task is very challenging, because event causality is usually expressed in diverse forms that often lack explicit causal clues. Existing methods cannot handle well the problem, especially in the condition of lacking training data. Nonetheless, humans can make a correct judgement based on their background knowledge, including descriptive knowledge and relational knowledge. Inspired by it, we propose a novel Latent Structure Induction Network (LSIN) to incorporate the external structural knowledge into this task. Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge. To leverage the relational knowledge, we propose a Relational Graph Induction module which is able to automatically learn a reasoning structure for event causality reasoning. Experimental results on two widely used datasets indicate that our approach significantly outperforms previous state-of-the-art methods.

## introduction event causality identification (eci) aims to identify causal relation of events in texts. for example, in the sentence "the earthquake generated a tsunami.", an eci model should be able to identify a causal relationship that holds between the two mentioned events, i.e., earthquake cause ---→ tsunami. eci is an important task in natural language processing (nlp) area and can support many nlp applications, such as machine reading comprehension @xcite , process extraction @xcite and future event prediction @xcite . identifying event causal relation is inherently challenging, because event causality is usually expressed in diverse forms that often lack explicit clues indicating its existence. for example in figure 1 , the sentence has no explicit clue indicating the causal relation between "global warming" and "tsunami". in this scenario, models can resort to a large amount of labeled data to learn diverse causal expressions. however, existing eci datasets are very small. for example, the largest dataset eventstoryline @xcite only contains 258 documents, which is not sufficient to train neural network models @xcite . consequently, models cannot thoroughly understand the text and possibly make a wrong prediction. nonetheless, humans could make a correct judgement, because humans have the background knowledge about the two events. to be more specific, humans not only know what the two events are, but also know the connection between them. fortunately, existing knowledge bases (kbs) usually contain the descriptive knowledge of events and relational knowledge between events, which can be regarded as the background knowledge to enhance eci models. in this paper, we focus on how to incorporate these two kinds of external knowledge into the task. descriptive knowledge: the external knowl-edge base contains the descriptive or explanatory information about events, which can be called the descriptive knowledge of events. it usually consists of one-hop neighbors of events. this kind of knowledge is able to help the model better understand what the mentioned event is. for example in figure 1 , the descriptive knowledge associated with "global warming" includes (global warming, isa, temperature change), (global warming, createdby, greenhouse gas) and so on. if the model can make use of such knowledge, it is obvious that the model can better understand the meaning of the event itself than using only the given text. therefore, incorporating the descriptive knowledge is very helpful for this task. however, when leveraging this kind of knowledge, we find two critical challenges: (1) as shown in figure 1 , the descriptive knowledge forms a sub-graph. how to effectively encode the graph-structured knowledge is a very challenging problem; ## related work event causality identification (eci) is a very important task in natural language processing area, which has attracted extensive attention in the past few years. early studies for the task are feature-based methods which utilize lexical and syntactic features @xcite , explicit causal patterns @xcite @xcite , and statistical causal associations @xcite @xcite for the task. with the development of deep learning, neural global warming worsened, and tsunami strengthened. network-based methods have been proposed for the task and achieved the state-of-the-art performance @xcite @xcite . @xcite propose a mention masking generalization method and also consider the external structural knowledge. the very recent work @xcite propose a data augmentation method to alleviate the data lacking problem for the task. regarding datasets construction, @xcite annotates the causal-timebank dataset about event causal relations in the tempeval-3 corpus. caselli and vossen (2017) construct a dataset called eventstoryline for event causality identification. despite many efforts for this task, most existing methods typically train the models on manually labeled data solely, rarely considering the external structural knowledge. as a result, these methods cannot handle well the cases where there is no explicit causal clue. although @xcite leverage the descriptive knowledge to enrich event representations, they directly retrieve the descriptive knowledge from the kb. therefore, their method cannot handle the cases where there is no knowledge about the event in the kb. in addition, they ignore the relational knowledge between events. by contrast, our method can not only generate the descriptive knowledge when it cannot be retrieved from the kb, but also leverage the relational knowledge. to our knowledge, we are the first to simultaneously make use of the descriptive knowledge and relational knowledge for this task. ## methodology following previous works @xcite , we formulate eci as a binary clas-sification problem. for every pair of events in a sentence, we predict whether a causal relation holds. figure 2 schematically visualizes our approach, which consists of three major components: ## conclusion in this paper, we propose a novel latent structure induction network (lsin) to leverage the external structural knowledge for eci task. to make use of the descriptive knowledge, we devise a descrip-tive graph induction module to obtain and encode the graph-structured descriptive knowledge. to utilize the relational knowledge, we propose a relational graph induction module to induce a more reasonable reasoning structure for causality reasoning. experimental results on two widely used datasets indicate that our approach substantially outperforms previous state-of-the-art methods.