Incremental Event Detection via Knowledge Consolidation Networks (2020)

Abstract: Conventional approaches to event detection usually require a fixed set of pre-defined event types. Such a requirement is often challenged in real-world applications, as new events continually occur. Due to huge computation cost and storage budge, it is infeasible to store all previous data and re-train the model with all previous data and new data, every time new events arrive. We formulate such challenging scenarios as incremental event detection, which requires a model to learn new classes incrementally without performance degradation on previous classes. However, existing incremental learning methods cannot handle semantic ambiguity and training data imbalance problems between old and new classes in the task of incremental event detection. In this paper, we propose a Knowledge Consolidation Network (KCN) to address the above issues. Specifically, we devise two components, prototype enhanced retrospection and hierarchical distillation, to mitigate the adverse effects of semantic ambiguity and class imbalance, respectively. Experimental results demonstrate the effectiveness of the proposed method, outperforming the state-of-the-art model by 19% and 13.4% of whole F1 score on ACE benchmark and TAC KBP benchmark, respectively.

## introduction event detection (ed) is an important task of information extraction, which aims to identify event triggers and classify them into specific types @xcite . for instance, in the sentence "a man died when a tank fired on the hotel", an ed model should be able to recognize two events: a die event triggered by the word "died" and an attack event triggered by the word "fired". existing ed methods can only handle a fixed number of event classes (also called event types) and perform once-and-for-all training on a fixed dataset. although the evaluation of this setting is straightforward, it clearly limits the usage of these methods in practical applications, as new events continually emerge in the real world. a practical ed system should be able to incrementally learn new classes, instead of requiring a fixed set of pre-defined event classes. therefore, we consider a more realistic incremental learning setting (also called continual learning or lifelong learning) @xcite , where a learning system learns from class-incremental data streams in which examples of different event classes arrive at different times, which can be shown in figure 1 . in such scenarios, it is often infeasible to combine the new data with all previous data to re-train the model, due to various issues such as huge computation cost, storage budget and data privacy @xcite . alternatively, a natural approach to incremental event detection is to simply finetune a pre-trained model on new data. however, this approach faces a serious challenge -catastrophic forgetting @xcite . to be more specific, a learned system usually suffers from significant performance drop on old classes when it adapts to new classes. for example in figure 2 , after finetuning on the training data of new class (i.e., attack), the updated model can recog-a man died when a tank fired on the hotel. ## method we propose a knowledge consolidation network (kcn) for incremental event detection, which is illustrated in figure 3 . the model consists of three important components: 1) trigger extractor, 2) prototype enhanced retrospection and 3) hierarchical distillation. when new classes occur, the proposed kcn model updates the parameters with the combination of reserved old classes data and training data of new arriving classes. the prototype enhanced retrospection retains previous knowledge via reserving the most representative examples. the hierarchical distillation transfers the previous knowledge from the original model to the current model. we will detail these three components. ## related work in this section, we briefly review two related topics: event detection and incremental learning. ## conclusion in this paper, we introduce incremental learning into event detection and propose a knowledge consolidation network to preserve previously learned knowledge. to alleviate semantic ambiguity, we devise the prototype enhanced retrospection to reserve the most representative examples. moreover, to mitigate the adverse effect of class imbalance, we propose the hierarchical distillation to learn the previous knowledge from the original model. experimental results demonstrate that our model outperforms previous state-of-the-art methods.