Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss (2022)

Abstract: In document-level event argument extraction, an argument is likely to appear multiple times in different expressions in the document. The redundancy of arguments underlying multiple sentences is beneficial but is often overlooked. In addition, in event argument extraction, most entities are regarded as class “others”, i.e. Universum class, which is defined as a collection of samples that do not belong to any class of interest. Universum class is composed of heterogeneous entities without typical common features. Classifiers trained by cross entropy loss could easily misclassify the Universum class because of their open decision boundary. In this paper, to make use of redundant event information underlying a document, we build an entity coreference graph with the graph2token module to produce a comprehensive and coreference-aware representation for every entity and then build an entity summary graph to merge the multiple extraction results. To better classify Universum class, we propose a new loss function to build classifiers with closed boundaries. Experimental results show that our model outperforms the previous state-of-the-art models by 3.35% in F1-score.

## introduction event argument extraction (eae) is a crucial subtask of event extraction (ee), aiming to identify the arguments of a given event and recognize the specific roles they play. previous works are mostly focused on sentence-level ee @xcite @xcite @xcite @xcite . however, events are often described in the form of documents in the real world. document-level event extraction has received consideration in recent years. research on document-level event extraction has been focused on tackling challenges such as * corresponding author. arguments-scattering and multiple-events @xcite @xcite @xcite @xcite @xcite . the benefit of redundant event information in a document is largely neglected. we believe that the redundant event information in a document can be used to improve event extraction, as illustrated in the example in figure 1 . the upper part of figure 1 shows seven simplified sentences selected from a document in the muc-4 dataset. all entities marked in blue are the same entity "soldiers", which appears in different expressions in different sentences. for ease of description, we call it entity s. we can observe from figure 1 that: 1) the argument information in the document is redundant since entity s appears in the article multiple times as an argument and we can successfully extract the argument by correctly recognizing any of these occurrences. this property can be potentially used to improve the robustness of the model. 2) the difficulty of extracting the entity s as an argument in its different occurrences is different. extracting entity s in sentence 1 and sentence 3 is much easier than extracting it from sentence 2. hence, by utilizing the redundant event information of the document, we can extract arguments from relatively simple positions and reduce the difficulty of the task. 3) an entity may appear multiple times in the document, directly averaging them as the entity's feature representation @xcite may introduce noise. for example, although entity s is an event argument in the document, its occurrences in s4, s5 and s6 should not be recognized as a correct pattern to identify the event argument. 4) the redundant argument information can result in redundant extraction results, as shown in the bottom table in figure 1 . the three entities extracted as perpetrator individual need to be merged into one. however, the extracted physical target "houses" and "library" are different entities and should not be merged. therefore, the use of redundant event information underlying a document is not straightforward, a sophisticated algorithm for merging multiple extraction results is needed. extraction of arguments can be solved as an entity classification problem by treating entities as argument candidates @xcite @xcite . in document-level event argument extraction, only a subset of the entities in a document are arguments, while the majority of entities are regarded as class "others" or "neither"(neither of the target classes). this kind of data was first studied by @xcite under the name universum. the universum data are usually very diverse and do not have typical common features. in addition, universum data is much more than the target class data, exhibiting a severe class imbalance problem. figure 2 demonstrates a simplified distribution of data samples in documentlevel event argument extraction. the blue dots represent argument entities, the orange dots represent a large number of universum class entities. since the samples in the universum class do not have typical common features, they tend to scatter in the feature space. this characteristic of the universum data is largely overlooked in the information extraction community. universum data is simply considered as another class "others", without any special consideration in the classifier design. cross entropy loss is usually employed in classifier training @xcite @xcite . however, classifiers trained by cross entropy loss have open decision boundaries, and hence some universum samples, such as the orange dot on the upper right of the figure, could be easily misclassified. we think a classifier with a closed decision boundary could better deal with the universum class in documentlevel event argument extraction, as illustrated by the purple line in figure 2 . the contribution of this work is three-fold. firstly, it is the pioneering work to leverage redundant event information in documents for event extraction. we propose the entity coreference graph with graph2token module and entity summary graph to leverage the redundant event information. experimental results show that redundant information helps improve recall significantly. secondly, we analyze the issue of universum data in document-level event argument extraction and the problem of classifiers trained by cross entropy loss, and propose a closed boundary loss to address the problems. finally, our model consistently outperforms the latest baseline models in f1-score and achieves state-of-the-art performance. compared to the three baseline models, our proposed model improves the absolute f1-score by 3.35%, 5.27%, and 6.45%, respectively. ## method as shown in figure 3 , our model consists of four main components: context encoding module (sec 3.1), entity coreference graph (sec 3.2), closed boundary loss (sec. 3.3), and entity summary graph (sec. 3.4), which are illustrated in this section. ## conclusion and future works in this work, we emphasize that the redundant event information in documents is beneficial but is often overlooked in document-level eae. in addition, we find that classifiers trained by cross entropy loss are problematic in classifying the universum class. specifically, we generate a comprehensive and coreference-aware representation for every entity through the entity coreference graph with the graph2token module. in addition, we propose an entity summary graph to merge the multiple extraction results of the same argument. furthermore, we propose a novel closed boundary loss to deal with the universum class in classification. as a limitation, our proposed closed boundary loss is used for binary classification because we extract arguments in a role-by-role manner to make full use of the property of each argument role. in the future, we will extend it for multiclass classification and apply it to other tasks in natural language processing that face the problem of classifying universum class. experimental results show that our ricb model achieves the sota performance and outperforms prior approaches on the muc-4 dataset.