Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction (2021)

Abstract: Implicit Event Argument Extraction seeks to identify arguments that play direct or implicit roles in a given event. However, most prior works focus on capturing direct relations between arguments and the event trigger. The lack of reasoning ability brings many challenges to the extraction of implicit arguments. In this work, we present a Frame-aware Event Argument Extraction (FEAE) learning framework to tackle this issue through reasoning in event frame-level scope. The proposed method leverages related arguments of the expected one as clues to guide the reasoning process. To bridge the gap between oracle knowledge used in the training phase and the imperfect related arguments in the test stage, we further introduce a curriculum knowledge distillation strategy to drive a final model that could operate without extra inputs through mimicking the behavior of a well-informed teacher model. Experimental results demonstrate FEAE obtains new state-of-the-art performance on the RAMS dataset.

## introduction in this work, we investigate the problem of implicit event argument extraction (ieae) @xcite , which seeks to identify arguments that play specific roles respect to a given trigger @xcite . unlike previous event argument extraction task that only processes a single sentence, arguments in ieae could span multiple sentences. as shown in figure 1 , given a conflict/attack/firearmattack event triggered by the word shooting, an ieae system is required to extract four corresponding arguments with their roles in brackets: mass murder (target), firearms (instrument), andrey shpagonov (attacker), and tatarstan (place). ## method our feae framework consists of two training steps to drive a model that could utilize framelevel knowledge for ieae, and details are shown in figure 2 . for single teacher situations, firstly we train an mrc-based teacher model m t with oracle knowledge composing of golden-standard relevant arguments to exploit frame-aware information and obtain the capacity to reason. then a student model m s that does not have access to this oracle information is driven with the guidance of m t to be used in practice. our framework can also be extended to multi-teacher circumstances. in the following sub-sections, we will give the formulation of our task and our mrc-based model. after that, we will illustrate the curriculum knowledge distillation strategy to bridge the gap between the training and inference stage. ## conclusion and future work in this paper, we exploit frame-aware knowledge for extracting implicit event arguments. specifically, we introduce a curriculum knowledge distillation strategy, feae, to train an mrc model that could focus on frame-aware information to identify implicit arguments. the proposed method leverages a teacher-student framework to avoid the requirement of extra clues and could perform reasoning with the guidance in event frame-level scope. experiments show that our method surpasses strong state-of-the-art baselines in rams, and could scientifically alleviate long-range dependency and implicit argument problems.