Explicit Role Interaction Network for Event Argument Extraction (2022)

Abstract: Event argument extraction is a challenging subtask of event extraction, aiming to identify and assign roles to arguments under a certain event. Existing methods extract arguments of each role independently, ignoring the relationship between different roles. Such an approach hinders the model from learning explicit interactions between different roles to improve the performance of individual argument extraction. As a solution, we design a neural model that we refer to as the Explicit Role Interaction Network (ERIN) which allows for dynamically capturing the correlations between different argument roles within an event. Extensive experiments on the benchmark dataset ACE2005 demonstrate the superiority of our proposed model to existing approaches.

## introduction event extraction is an important research field in information extraction, and its purpose is to extract structured information describing events from natural language texts. event extraction includes two sub-tasks, namely (1) event detection (ed): trigger recognition and event type classification, and (2) event argument extraction (eae): argument identification and role classification. for example, given a sentence "a group of soldiers were attacked on friday.", it describes an attack event, triggered by the word "attacked" and accompanied by 2 arguments: "a group of soldiers" and "friday", which play the role of victim and time respectively. event detection is a prerequisite for event extraction. the technologies and methods in this task are relatively established, and there has been great advancement in recent years @xcite @xcite . however, the progress in event argument extraction is still the us president @xcite first visited japan since his appointment @xcite showing ties between tokyo and washington @xcite . tokyo washington (a) the person argument "us president" helps exclude the interfering entity "tokyo" and implies the golden argument "washington" of entity. the building in the capital @xcite [target] took a direct hit @xcite , glass across the street in the neighborhood @xcite . figure 1 : two examples of eae demonstrating the importance of role interaction. the trigger words, followed by its event type in "[]", are marked in green. the arguments to be extracted are marked in red, followed by its role type in "[]" while the relevant arguments for interaction are marked in blue. most existing methods for event argument extraction are neural network based, modeling the task as a sequence labeling problem and focusing on learning expressive features from text @xcite @xcite ). an issue that has to be addressed for those methods is that arguments of different roles for an event may overlap with each other. thus, it is not practical to extract all arguments simultaneously. as a consequence, majority of approaches are designed to extract arguments of each role independently @xcite @xcite . 2 however, such an approach addresses arguments of each role in isolation, hindering model from learning the correlation between roles. we argue that it is important to consider role interactions for event argument extraction. on the one hand, the arguments of one role may closely relate to the arguments of another role. entities that interfere with the argument to be extracted can be excluded by using information from other related arguments. as an example shown in figure 1 (a), when identifying the arguments of entity for the start position event, "tokyo" and "washington" both have a chance of becoming candidate arguments. the introduction of interaction with the person argument "us president" will provide clues for the model to identify the correct argument "washington" since "washington" is the semantically relevant argument with "us". meanwhile, the model without the interaction wrongly extracts the "tokyo" as the argument. on the other hand, the arguments of some related roles may overlap with each other. learning the interaction between those overlapping arguments can help improve the individual extraction of those arguments. as an example shown in figure 1(b) , the target argument "the building in the capital" and the place argument "capital" share a nested structure. both target and place roles indicate the location-related argument. the model without interaction fails to identify the inner argument "capital". by introducing the interaction with the target argument, the model will be guided to pay more attention to content in that text span of the target argument, since there might be another locationrelated argument within the span. then the model successfully extract the "captital" argument. based on these observations above, we propose a novel explicit role interaction network (erin) for event argument extraction. the main contribution in erin is the proposed role interaction module (rim) which serves to dynamically capture the correlations between different roles in an event. specifically, rim learn interactions in an explicit way by considering prediction outputs of argument extraction, allowing argument spans to be mutually aware and establishing an information transmission between roles. besides, a transformer in rim enables the learning of a global interaction across roles. the rim is also designed as a multi-layer architecture to match the requirement same role, otherwise they will be merged into one argument. for gradually learning more complicated interaction cues, resulting in more refined results of argument extraction. to verify the effectiveness of our model, we conduct extensive experiments on the benchmark ace2005 dataset. our model attains state-of-the-art performance, and improves the f1 score of argument extraction to 57.96% (+2.96%). ## related work the mainstream methods for event extraction can be categorized into two classes: the pipeline models @xcite @xcite @xcite , which feeds results of ed to the downstream task eae, and the joint models @xcite @xcite , which extracts triggers and arguments simultaneously. under the pipeline approaches, @xcite @xcite are question answering based models that search answers to questions as results of extraction. @xcite conducts data augmentation for insufficient training corpus. among joint models, @xcite and @xcite propose to leverage interactions between arguments. @xcite incorporates both event-specific and syntactic information. @xcite ) transforms text sequences into event structures. some methods also integrate named entity recognition (ner) as additional supervision @xcite @xcite @xcite . though joint models alleviate the error propagation existing in pipelinebased methods, designing the optimal joint way of interdependence between two tasks is complex, which frequently results in a substandard architecture that is difficult to optimize @xcite . our work follows the pipeline-based approaches, but focuses on modeling interactions between roles for event argument extraction. so far, a majority of works have been conducted to improve performance in many fields by exploiting explicit interactions between different kinds of information in the task, such as relation extraction @xcite , sentiment analysis @xcite and ner @xcite , while only few works have explored interactions between roles for event argument extraction @xcite . among these works, @xcite define the interaction as positive and negative correlation, which only determines whether or not this argument is to be recognized. @xcite calculate the interaction vectors between each pair of candidate arguments, and add a self-matching matrix to judge whether two arguments tend to occur together. however, these works focus on learning interactions based on intermediate representations of networks, ignoring the interdependence between prediction outputs of argument extraction. in this paper, we consider the classifier outputs of argument extraction to provide an explicit supervision signal for model to learn the interactions. ## problem definition in this section, we formally describe the event argument extraction (eae) task. given an event text @xmath0, x 2 , . . . , x n } of n words, the event trigger t is a text span in x, which indicates an event type e @xmath1, where @xmath2, ..., e u } is the set of all u different event types. given a triple (x, t, e t ), the goal of eae is to extract all the arguments related to t in x, and assign a role @xmath3, where r @xmath4, ..., r m } is the set of m different roles associated with the event type e t , and the argument a t is a text span in x. the event schema is usually pre-defined, and each type of event contains a set of roles. ## model in this section, we introduce our proposed explicit role interaction network (erin) for event argument extraction. the overview of the network is shown in figure 2 . first we introduce the text encoder of erin which we refer as role-aware encoder (rae). next we introduce the proposed role interaction module (rim) which is designed to capture the correlations between different roles in an event. then we introduce the training objective of erin and briefly present our model for event detection. ## conclusion and future work in this paper, we propose a novel model for the task of argument extraction, referred to as explicit role interaction network, to capture the correlation between different argument roles within the same event. the model is able to generate roleaware text representations and explicitly create roleinformation transmission between the predicted positions of two arguments in the sequence using the gru network, enabling argument spans to be mutually aware. this structure not only aids in the detection of overlapping arguments but also dynamically adjusts and refines the identification results based on the multi-layer interaction. extensive experiments on ace 2005 demonstrate the effectiveness of this method. in future, we intend to use the sota model of event detection @xcite to obtain reliable predicted triggers as the input for argument extraction, to explore the upper bound performance of our proposed method. in addition, we will extend the explicit interaction method to other ie tasks, such as document event extraction and relation extraction, to study its application scope.