Event Detection with Dual Relational Graph Attention Networks (2022)

Abstract: Event detection, which aims to identify instances of specific event types from pieces of text, is a fundamental task in information extraction. Most existing approaches leverage syntactic knowledge with a set of syntactic relations to enhance event detection. However, a side effect of these syntactic-based approaches is that they may confuse different syntactic relations and tend to introduce redundant or noisy information, which may lead to performance degradation. To this end, we propose a simple yet effective model named DualGAT (Dual Relational Graph Attention Networks), which exploits the complementary nature of syntactic and semantic relations to alleviate the problem. Specifically, we first construct a dual relational graph that both aggregates syntactic and semantic relations to the key nodes in the graph, so that event-relevant information can be comprehensively captured from multiple perspectives (i.e., syntactic and semantic views). We then adopt augmented relational graph attention networks to encode the graph and optimize its attention weights by introducing contextual information, which further improves the performance of event detection. Extensive experiments conducted on the standard ACE2005 benchmark dataset indicate that our method significantly outperforms the state-of-the-art methods and verifies the superiority of DualGAT over existing syntactic-based methods.

## introduction event detection (ed) aims to identify event triggers from a given text and classify them into predefined event types @xcite , playing an important role in information extraction. as shown in figure 1 , an event detection method is expected to identify the event trigger "thrust" from the example sentence and classify it into the event type transport. event detection can be used to facilitate various natural language processing * corresponding author (nlp) applications, such as adverse drug event discovery @xcite , court decision event identification @xcite , financial event extraction @xcite and so on. various event detection methods have been explored, including traditional feature-based @xcite and deep learning methods @xcite . due to the powerful feature representation extraction capability of deep neural networks, deep learning methods have outperformed traditional feature-based methods in most cases and have become popular. most deep learning methods have recently paid more attention to exploiting syntactic relations in event detection. these methods adopt graph neural networks (gnns) such as graph convolutional networks (gcns) and graph attention networks (gats) to encode dependency trees to learn effective representations for the words. since dependency trees convey rich linguistic information for ed, syntactic-based methods usually achieve better performance @xcite . however, existing syntactic-based methods still have two shortcomings to be solved. first, dependency trees cannot always capture trigger-related salient information concisely, which may contain noisy dependency relations close to the root node and mislead event detection @xcite . as shown in figure 1 , the dependencies marked in red are incorrectly identified by the syntactic-based event detection methods as trigger-related hints for the attack event. since "troops" and the trigger candidate "striking" have a direct connection with the root node "distance", gnn-based methods are prone to pay more attention to them and predict incorrect triggers @xcite . it is worth noting that in this case, the marked blue dependencies that are closely related to the real trigger "thrust" should be exploited with more emphasis. second, relying solely on syntactic dependency trees may not be sufficient for event detection. the parsing results of the existing dependency parser may contain incorrect or weakly correlated information, which will inevitably affect the performance of syntactic-based event detection methods due to possible error propagation. moreover, syntactic dependency trees cannot provide all the linguistic knowledge needed for event detection. to address the above problems, we propose a novel model named dualgat, which makes full use of syntactic and semantic information to improve event detection performance. inspired by aspect-based dependency parsing @xcite , we construct a dual relational graph that contains both syntactic and semantic relations to capture trigger-relevant information. empirically, only a small part of dependency relations in a sentence is task-aware @xcite . therefore, to reduce the influence of noisy relations, we prune the original syntactic dependencies that are not directly connected to the trigger candidates and reconstruct other connections between the remaining words of the sentence and the trigger candidates. in addition to syntactic information, we also introduce semantic relation information and make them rooted in the predicate of the sentence. next, we adopt augmented relational graph attention networks to encode the graph to learn the root node's representations from syntactic and semantic views. specially, we introduced contextual information to adjust the attention weights to mitigate the possible loss of information due to the introduction of the dependency parser. the experimental results on the standard ace2005 benchmark dataset indicate that dualgat outperforms the state-of-the-art methods by a large margin. the main contributions of this work can be summarized as follows: dualgat achieves the 5% improvements in f1 score with state-of-the-art syntactic-based methods. ## related work event detection has attracted extensive attention in recent years. traditional event detection methods use hand-crafted linguistic features for event detection, such as lexical features, syntactic features, or entity features @xcite . however, it is time-consuming to design these features and is not easy to adapt to other tasks or new domains. with the rapid development of neural networks, a series of neural event detection methods have been proposed. many researchers applied new learning strategies to event detection, such as leveraging the weakly-supervised learning strategy to generate more labeled data to improve the performance of event detection @xcite . @xcite applied an adversarial training mechanism to obtain diverse and accurate training data. @xcite proposed a method based on knowledge distillation to achieve better performance on sparsely labeled triggers. some recently proposed methods introduced extra knowledge to improve event detection via open-domain trigger knowledge @xcite , entity knowledge @xcite and syntactic dependency relations @xcite . the effectiveness of dependency relations has been verified in many natural language processing (nlp) tasks, such as sentiment analysis @xcite and relation extraction @xcite . due to the rich syntactic and structure information, syntactic dependency relations also play an important role in event detection @xcite . for example, @xcite al. (2019 ) exploited multi-order syntactic relations in sentences to obtain better representations of trigger words. lv et al. (2021) integrated syntax and document information for better event detection. cui et al. (2020) proposed a model to explore further the type information of dependency relations to capture task-aware knowledge. since existing graph-based models introduced many trigger-agnostic representations, lai et al. (2020) proposed to filter noisy information via a gating mechanism. due to the effective combination of gnns and syntactic dependency trees, these syntactic-based event detection methods achieved overall better performance than ordinary deep learning methods. although these works use similar syntactic information, few of them take a new perspective to reshape the original graph to facilitate the capture of event-relevant salient information. the original dependency tree contains rich structural and linguistic knowledge, but it may be inaccurate and contains event-irrelevant information. besides, as far as we know, there are no syntactic-based approaches that explicitly use semantic information to complement syntactic information for event detection. to this end, we propose dualgat, which takes into account both syntactic and semantic relations as well as contextual information for event detection. ## conclusion and future work in this paper, we propose a simple yet effective model named dualgat (dual relational graph attention networks) to address the disadvantages of syntactic-based methods for event detection tasks. to facilitate the accurate capture of key information from different perspectives in a sentence, we devise a dual relational graph that aggregates syntactic and semantic relations to key nodes in the graph. to efficiently encode the dual relational graph, we propose augmented relational graph attention networks that introduce contextual information to compute more robust attention weights. experimental results show that our proposed method achieves state-of-the-art performance. we intend to explore several aspects of our work further in the future. first, we would improve the way semantic information is introduced. second, we would develop a more effective method for fusing syntactic and semantic information. third, we would explore the effect of augmented relational graph attention networks in other tasks.