What Is Overlap Knowledge in Event Argument Extraction? APE : A Cross-datasets Transfer Learning Model for EAE (2023)

Abstract: The EAE task extracts a structured event record from an event text. Most existing approaches train the EAE model on each dataset independently and ignore the overlap knowledge across datasets. However, insufficient event records in a single dataset often prevent the existing model from achieving better performance. In this paper, we clearly define the overlap knowledge across datasets and split the knowledge of the EAE task into overlap knowledge across datasets and specific knowledge of the target dataset. We propose APE model to learn the two parts of knowledge in two serial learning phases without causing catastrophic forgetting. In addition, we formulate both learning phases as conditional generation tasks and design Stressing Entity Type Prompt to close the gap between the two phases. The experiments show APE achieves new state-of-the-art with a large margin in the EAE task. When only ten records are available in the target dataset, our model dramatically outperforms the baseline model with average 27.27% F1 gain.

## introduction event extraction (ee) is a pivotal task in information extraction. typically, the event extraction task can be divided into two sub-tasks: event detection (ed) and event argument extraction (eae). thanks to recent works @xcite @xcite , event detection has achieved significant progress. the main challenge of ee lies in the eae task. the eae task aims to extract a structured event record from an event text. since different datasets often have various event types and argument structures, most studies @xcite @xcite ) train the eae model on each dataset independently, such as ace 2005 @xcite , rams @xcite , and wikievents @xcite . however, one single dataset often cannot provide sufficient event records, which seriously prevents those models from achieving better performance. especially in some industrial applications, the in-domain event record collection incurs expensive and timeconsuming manual annotation. we argue that there is abundant transferable all-purpose knowledge of the eae task among different datasets, called overlap knowledge. exploring the overlap knowledge from existing datasets can significantly improve the model's performance and reduce the need for newly annotated data. how to transfer knowledge across datasets has yet to be well studied. only @xcite attempted to introduce variational information bottleneck to retain the shared knowledge between two datasets and achieved considerable success. nevertheless, their model architecture restricts that they can only obtain overlap knowledge from up to two datasets. moreover, it has not explicitly defined what is the overlap knowledge among the different datasets. therefore, they use the eae task's training objective to train the model on two datasets jointly and roughly let the model distinguish what knowledge is shareable across datasets. the imprecise training objectives perplex the model to learn the overlap knowledge better. in this work, we propose a seek common ground while reserving differences (sc-rd) framework to define the overlap knowledge clearly. sc-rd suggests defining overlap knowledge based on a cross-dataset common ground and isolating other knowledge into specific knowledge. as shown in figure 1 , every argument role in different datasets can be attached to an entity type. we introduce a finite entity type set (shown in appendix we implement the above idea in ape, which assembles two parameter-efficient tuning methods to harmonize two parts of knowledge in one single model. specifically, we introduce two learning phases (illustration in figure 2 ) to learn overlap and specific knowledge, respectively. in the overlap learning phase, we merge multi-datasets and convert their unaligned eae labels to aligned per labels to optimize the prefix, which is introduced to save overlap knowledge. in the specific learning phase, we load and freeze the trained prefix and tune the adapter's parameters with the eae task in the target dataset to save specific knowledge. all the pre-trained model's parameters will be frozen like traditional parameter-efficient tuning methods. furthermore, to ensure the overlap knowledge plays a part in the eae task, we format both training tasks as conditional generation tasks and propose the stressing entity type prompt to ignite the overlap knowledge in the eae task. to the best of our knowledge, we are the first to clearly define the overlap knowledge across datasets, so we can give the model a transparent training objective to help it learn the overlap knowledge. our model expands parameter-efficient tuning methods to the transfer learning scene. since ape optimizes different parameters in two learning phases, learning the specific knowledge will not trigger catastrophic forgetting @xcite of the overlap knowledge. we have conducted extensive experiments on three widely used datasets. the experimental results show that our proposed ape outperforms baselines with a large margin (2.7%, 2.1%, 3.4% f1 gain absolutely on three benchmarks). moreover, it achieves 27.27% f1 score gain average over three datasets when only ten samples of the target dataset are available, indicating our model's fewshot learning ability. further analysis in section 4.3 confirms the efficacy of the main components in our model. ## method as illustrated in figure 2 , ape learns two parts of knowledge in two learning phases sequentially. to overcome catastrophic forgetting, our model (section 2.2) assembles prefix @xcite to save overlap knowledge and adapter @xcite to save specific knowledge, respectively. to fully use the overlap knowledge learned from multi-datasets, we carefully design the task formulation (section 2.1) and the stressing entity type prompt (section 2.3) of two learning phases. ## results and analyses to investigate the efficacy of our ape model, we compare our model with several state-of-the-art baseline models (4.1). then, we verify the significance of transfer overlap knowledge (4.2) in the few-shot setting. we also perform ablation studies and further analysis to examine the effectiveness of the main components in our model (4.3). ## conclusion in this work, we first define the shareable overlap knowledge across datasets and reformulate the eae task into two learning phases. then, we propose ape model, which assembles two parameterefficient tuning methods to save the overlap and specific knowledge. the experiment results show the efficiency of the cross-dataset transfer learning, and ape achieves new sota with a large margin in the eae task. our model significantly reduces the need for new event records and achieves superior performance with few samples of target datasets. the ablation studies verify that our approach can explore overlap knowledge from multi-datasets and overcome the well-known catastrophic forgetting issue. in the future, we would like to study overlap knowledge across datasets in other information extraction tasks.