Extracting Trigger-sharing Events via an Event Matrix (2022)

Abstract: A growing interest emerges in event extraction which aims to extract multiple events with triggers and arguments. Previous methods mitigate the problem of multiple events extraction by predicting the arguments conditioned on the event trigger and event type, assuming that these arguments belong to a single event. However, the assumption is invalid in general as there may be multiple events. Therefore, we present a unified framework called MatEE for trigger-sharing events extraction. It resolves the kernel bottleneck by effectively modeling the relations between arguments by an event matrix, where trigger-sharing events are represented by multiple cliques. We verify the proposed method on 3 widely-used benchmark datasets of event extraction. The experimental results show that it beats all the advanced competitors, significantly improving the state-of-the-art performances in event extraction.

## introduction event extraction (ee) is a structured prediction task that aims to recognize and extract the events in the text. an event of a particular event type typically contains an event trigger and several relevant arguments. the ee task has long been challenging in the information extraction field as it involves various sub-tasks, e.g., named entity recognition @xcite , relation extraction @xcite . the primary studies @xcite @xcite @xcite of ee focus on extracting single event by simply detecting and labeling the spans. then several improved methods were proposed for extracting multiple events @xcite @xcite @xcite @xcite . these models typically formulate the extraction process as a sequence of conditional predictions, i.e., first recognize the event trigger, categorize the event type, and then extract the corresponding arguments in the events conditioned on the event trigger and event type. however, they still cannot extract multiple events jointly and mitigate the problem of multiple events extraction by multiple fine-grained conditional predictions. they make an implicit assumption that with a finegrained condition (trigger and event type) there can be only one event, but such an assumption is generally invalid. even with the specified event trigger and event type, there can be multiple events. in fact, there are around 16% samples in the fewfc @xcite dataset violating this assumption. an example of such multiple events is illustrated in figure 1 . there is a common trigger "increased" for four events where two events have the same event type. therefore, the capability of joint extraction of such multiple events with the same trigger is indispensable. hillhouse increased its holdings of sofia by 10 million shares and smart energy by 3 million shares in q2. ## trigger-sharing events representation previous methods for multiple events extraction typically predict the arguments conditioned on the trigger and event type which are predicted ahead of arguments. for instance, mqaee @xcite predicts the arguments by constructing the questions with the trigger and event type. casee @xcite similarly predicts the spans of arguments with the conditional inputs. although the predicted arguments may belong to multiple events, these methods are unable to distinguish various events from these arguments. therefore, the capability of extracting multiple trigger-sharing events jointly is critical. event matrix. to this end, we propose a novel prediction formalism to represent multiple triggersharing events in a single event matrix, motivated by recent advances in the pair representation @xcite . the event matrix effectively represents the co-event relationship between the arguments. specifically, for a text with l tokens @xmath0, w 2 , ..., w l }, the event matrix @xmath1,j } is of size r l×l . with n a types of arguments roles, the grid m i,j can take the value in {0, 1, ..., n a + 1} where 0 denotes the blank tag, 1 denotes the span tag and {2, ..., n a +1} correspond to the n a argument roles such as "sub", "obj" and "stk". the span tag is used to identify the argument spans in the events. m i,j is 1 if and only if there is an argument that starts with j-th token and ends with i-th token. m i,j in {2, ..., n a + 1} is used to indicate the co-event relationship of arguments. first, it means that there are two arguments (a 1 , a 2 ) co-exist in the same event, a 1 ends with j-th token and a 2 starts with i-th token. we assume that every argument has a unique boundary so that the spans of a 1 and a 2 can be directly inferred by (i, j). second, its value indicates the argument role of a 1 so that we can align the spans with the argument roles. by this definition, the co-existence of argument pairs can determine all the arguments of a single event. it is because if the arguments are in the same event, every two arguments are connected and the arguments form a clique in the graph. therefore, we can use a maximal clique algorithm to recover the events from the event matrix. figure 2 shows an event matrix where the value of m i,j is denoted by various colors. let purple denote the argument role of "sub". the color of the grid ("yin', "wei") is purple since the argument ending with "yin" and the argument starting with "wei" are in the same event, and the former argument is a "sub". to derive the corresponding arguments "tian yin" and "wei ke" by "yin" and "wei", we can refer to the blue grids ("yin", "tian") and ("ke", "wei") which denote the spans of the arguments. taking the whole event matrix into consideration, we can know the "tian yin", "wei ke" and "20%" are in the same event and their argument roles are "sub", "obj" and "stk" by the color of grids. consequently, the two events {sub: "tian yin", obj: "wei ke", stk: "20%"} and {sub: "tian yin", obj: "tian long", stk: "30%"} can be represented by this single event matrix. ## the matee framework the architecture of our framework is illustrated in figure 3 , which mainly consists of three components. first, the widely-used pre-trained language model bert is used as the encoder to yield word representations. then a trigger extractor is used to extract the triggers based on sequence labeling. afterward, an argument extractor that contains a multi-head biaffine network is used to predict the event matrix by the maximal clique decoding. ## related work single event extraction. the primary studies typically considered the task as a sequence labeling problem, to assign each token a tag from a predefined scheme (e.g., bio). mainstream studies combine the crf @xcite with neural architecture, such as cnn @xcite , bi-directional lstm @xcite , and transformer @xcite . however, these methods fail to address the problem of multiple events. multiple events extraction. there have been several studies @xcite @xcite @xcite @xcite that cast multiple events extraction as a sequence of conditional predictions, i.e., recognizing trigger with event type first, and then extracting the corresponding arguments. conditions are used in a variety of ways, such as graph aggregation @xcite , graph representation @xcite , seq2seq @xcite , mrc @xcite , and cascade decoding @xcite . however, the above methods make an implicit assumption that with a finegrained condition (trigger and event type) there can be only one event. inspired by extracting overlapped ner @xcite and spo @xcite , we propose an event matrix to deal with trigger-sharing events. ## conclusion we have presented a unified framework matee based on the event matrix which is a novel formalism to represent multiple events jointly. our framework is useful for various events extraction, especially trigger-sharing events. the empirical comparison and the results of analytical experiments verify its effectiveness. beyond ee, our work may shed light on other complicated structured prediction tasks where the components are hard to predict sequentially. in the future, our work will focus on generalizing the matee to the case with multi-role arguments and the incorporation of the inherent prior constraints.