Reconstructing Event Regions for Event Extraction via Graph Attention Networks (2020)

Abstract: Event information is usually scattered across multiple sentences within a document. The local sentence-level event extractors often yield many noisy event role filler extractions in the absence of a broader view of the document-level context. Filtering spurious extractions and aggregating event information in a document remains a challenging problem. Following the observation that a document has several relevant event regions densely populated with event role fillers, we build graphs with candidate role filler extractions enriched by sentential embeddings as nodes, and use graph attention networks to identify event regions in a document and aggregate event information. We characterize edges between candidate extractions in a graph into rich vector representations to facilitate event region identification. The experimental results on two datasets of two languages show that our approach yields new state-of-the-art performance for the challenging event extraction task.

## introduction event extraction (ee), a challenging task in natural language processing, aims to extract key types of information (aka event roles, e.g., perpetrators and victims of an attack event) that can represent an event in texts and plays a critical role in downstream applications such as question answer @xcite and summarizing @xcite . existing research on ee mostly focused on sentence-level, such as the evaluation in automatic content extraction (ace) 2005 1 . however, an event is usually described in figure 1 : an example of document-level event extraction. we need to extract noun phrases from the document as role fillers for the event roles in the predefined event template. the uppercased noun phrases in the document are role fillers extracted by the sentence-level extractor. red phrases are correct while green phrases are noises compared to the standard in the template. there are two event regions in the sample document. multiple sentences in a document. as illustrated in figure 1 , relevant event information (noun phrases in green color) is scattered across the whole document. to extract event information accurately and comprehensively at document-level, it is necessary to understand the wider context spanning over multiple sentences. the existing approaches for event extraction (ee) often decompose the document-level ee into sentence-level ee, and extract candidate event role fillers from individual sentences one by one. the event role filler extractors often use extraction patterns @xcite or classifiers @xcite to identify typical local contexts containing a certain type of event role fillers. however, local event role filler extractors often produce many false candidates, e.g., the red noun phrases shown in the example document of figure 1 . as shown in the example, one document often mentions a target event multiple times and each time it takes one or more sentences to articulate the event. the target event role fillers tend to be mentioned in several groups of adjacent sentences, and we define those adjacent relevant sentences as different event regions. for example, in figure 1 , the document mentions the target event twice in two regions. the correct role fillers are crowding in the first event region s1, s2, s3 and the second one s5, s6 respectively. nevertheless, the sentence-level extractor will extract noise from both the event regions like house from s3 and irrelevant sentence like father in s4, destroying the layout of the original regions. many previous efforts try to avoid aggregating the noisy candidates by detecting such event regions. the popular approach is to apply sentential classification to filter the sentences and recognize role fillers from the chosen sentences @xcite . however, these approaches only detect regions at single sentence-level and ignore the crowding of relevant sentences. also, they also suffer from the accumulative error of sentential classification. for example, they may identify s2 as a relevant event region but s3 as irrelevant because they fail to take into account the similarity of s2 and s3. another solution proposed by @xcite tries to detect the primary event description sentence and supplement the missing event roles with fillers from adjacent sentences. this method considers the multiple sentences in an event region but is limited to one region per document. for instance, it may detect s1 as the primary sentence and supplement it with s2, missing the valid items like shining path from region 2. moreover, it also suffers from the errors selecting primary sentence, and the supplementing strategy is coarse-grained and fails to take into account every candidate filler individually. we build a graph for each document to directly model the multiple event regions in a document, each region potentially consisting of multiple sentences. in each document graph, the nodes are candidate event role fillers and we insert an edge between two nodes based on either positional proximity (in adjacent sentences or within the same sentence) or the coreference relation between two candidate extractions. the document graphs capture sentence similarities and sophisticated discourse connections among the candidate event role fillers to reconstruct the original event regions, which can recognize false event role filler extractions from irrelevant sentences. for example, after identifying the differences between s4 and adjacent sentences s3 and s5, our model will filter the noisy candidate father in s4. furthermore, constructing document graphs formed by candidate event role fillers and applying graph neural networks will enable recognizing false event role filler extractions within an event region. we employ attentional networks on the graphs to reinforce each candidate's representations by global contextual information and then classify the candidates in a fine-grained manner. specifically, we characterize the edges into vector representations with rich features to control the information flowing between any two nodes. for instance, this mechanism will be likely to recognize that it is a murder event based on the sentential contexts of sentences s2 and s3, and therefore determine that the candidate extraction house is a false extraction because the targets of a murder are individuals most commonly, but not physical targets or buildings. we evaluate our approach on two documentlevel event extraction datasets: the muc-4 dataset and a newly created dataset cfeed foot_0 . experimental results show that the proposed approach successfully reconstructs 70% of the event regions and yields new state-of-the-art performance for event extraction on both datasets. in summary, the main contributions of this paper are as follows: ## related work sentence-level ee has achieved a lot of advancement in recent work @xcite @xcite and can be classified into template-based approaches @xcite @xcite and statistical approaches. templatebased methods require human-crafted templates to match the events. most of the statistical methods are supervised and either based on feature engineering @xcite @xcite or neural network algorithm @xcite @xcite @xcite . however, these supervised methods rely on intensive manual annotations. to alleviate this problem, many weak supervised methods @xcite have arisen and achieved good performance in ace 2005 evaluation. however, most of the time, people care about the events discussed across a whole document. so research on document-level ee also prevails. traditionally, pattern-based and classifier-based methods are popular to solve this task. systems like autoslog @xcite and autoslog-ts @xcite directly applied regular patterns to extract role fillers. many works @xcite @xcite @xcite relied on feature-based classifiers to distinguish candidate role fillers from texts and achieved better performance. until recent years, researchers @xcite @xcite began to utilize multiple neuralbased methods to solve the task. notably, among the document-level ee research, some works @xcite @xcite have noticed the importance of identifying event regions to improve performance. traditional neural networks such as convolutional neural networks and recursive neural networks are hard to deal with graphical data structures, so many graph-based neural networks (gnns) emerge @xcite @xcite . in order to deal with graphs with different edge types, relational gnns @xcite @xcite try to use separate weights for different edges. however, one limitation of these gnns is that the weights are fixed for all neighbors. so @xcite leveraged masked attentional layers (gats) to learn adaptive weights for different neighbors. by now, some works @xcite have successfully applied gnns to model the document-level information within texts and achieved state-of-the-art performance. our model is distinguishing because we not only utilize these recent advances but also turns the relational edges to feature-enriched nodes and extends gats on such heterogeneous graphs. ## conclusion we propose a fine-grained filtering framework to address the aggregating problem in document-level 819 event extraction by reconstructing event regions. our method can filter those noise both in irrelevant sentences and in the event regions and achieve stateof-the-art performance on both the muc-4 and cfeed datasets. future work may consider using an end2end model to avoid error propagation from see.