Event argument extraction (EAE) identifies event arguments and their specific roles for a given event. Recent advancement in generation-based EAE models has shown great performance and generalizability over classification-based models. However, existing generation-based EAE models mostly focus on problem re-formulation and prompt design, without incorporating additional information that has been shown to be effective for classification-based models, such as the abstract meaning representation (AMR) of the input passages. Incorporating such information into generation-based models is challenging due to the heterogeneous nature of the natural language form prevalently used in generation-based models and the structured form of AMRs. In this work, we study strategies to incorporate AMR into generation-based EAE models. We propose AMPERE, which generates AMR-aware prefixes for every layer of the generation model. Thus, the prefix introduces AMR information to the generation-based EAE model and then improves the generation. We also introduce an adjusted copy mechanism to AMPERE to help overcome potential noises brought by the AMR graph. Comprehensive experiments and analyses on ACE2005 and ERE datasets show that AMPERE can get 4% - 10% absolute F1 score improvements with reduced training data and it is in general powerful across different training sizes.

翻译：事件论元提取（EAE）旨在识别给定事件的论元及其特定角色。近年来，基于生成的EAE模型展现出相较于分类模型的优异性能与泛化能力。然而，现有生成式EAE模型主要聚焦于问题重构与提示设计，未融合已被证明对分类模型有效的额外信息（例如输入片段的抽象意义表示）。由于生成式模型普遍采用的自然语言形式与AMR的结构化形式存在异构性，将此类信息融入生成式模型颇具挑战性。本研究探索了将AMR融入生成式EAE模型的策略，提出AMPERE方法，为生成模型的每一层生成AMR感知前缀，从而将AMR信息引入生成式EAE模型以优化生成过程。我们还在AMPERE中引入了调整后的复制机制，以克服AMR图谱带来的潜在噪声。在ACE2005与ERE数据集上的全面实验与分析表明，AMPERE在减少训练数据的情况下可提升4%-10%的绝对F1值，且在不同训练规模下均展现出强大性能。