AMPERE : AMR -Aware Prefix for Generation-Based Event Argument Extraction Model (2023)

Abstract: 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.

## introduction event argument extraction (eae) aims to recognize event arguments and their roles in an event. for example, in figure 1 , eae models need to extract districts, u.s. supreme court, and washington and the corresponding roles -plaintiff, adjudicator, and place for the justice:appeal event with trigger appeal. eae has long been a challenging task in nlp, especially when training data is limited @xcite . it is * the authors contribute equally. recently, generation-based eae models @xcite @xcite @xcite are proposed and have shown great generalizability and competitive performance compared to traditional classificationbased methods @xcite @xcite . however, existing generation-based eae models mostly focus on problem reformulation and prompt design without incorporating auxiliary syntactic and semantic information that is shown to be effective in classification-based methods @xcite @xcite @xcite . in this work, we explore how to incorporate auxiliary structured information into generation-based eae models. we focus on abstract meaning representation (amr) @xcite , which extracts rich semantic information from the input sentence. as the figure 1 's example shows, amr graph summarizes the semantic structure of the input passage, and many of its nodes and edges share strong similarities with the event structures. for example, the trigger word appeal can be mapped to the node "appeal-01", and the subject who appeals can be found using edge "arg0". hence, the amr graph could provide important clues for models to figure out event arguments, resulting in performance improvements @xcite and better generalizability @xcite for classification-based methods. however, it is unclear how to best integrate amr into generationbased methods. the heterogeneous nature between the amr graph and the natural language prompts foot_0 in the generation-based eae models causes the ## experiments we conduct experiments to verify the effectiveness of ampere. all the reported numbers are the average of the results from three random runs. ## analysis in this section, we present comprehensive ablation studies and case studies to validate our model designs. two essential parts of our design, the amraware prefix, and the adjusted copy mechanism will be examined in the following studies. for all the experiments in this section, we use the setting of training on 5% and 20% ace05-e data to simulate very low-resource and low-resource settings. ## conclusion in this paper, we present ampere, a generationbased model equipped with amr-aware prefixes. through our comprehensive studies, we show that prefixes can serve as an effective medium to connect amr information and the space of generative models, hence achieving effective integration of the auxiliary semantic information to the model. additionally, we introduce an adjusted copy mechanism to help ampere more accurately and stably generate output disregarding the additional noise brought from the amr graph. through our experiments, we show that ampere achieves consistent improvements in every setting, and the improvement is particularly obvious in low-resource settings.