A Compressive Memory-based Retrieval Approach for Event Argument Extraction
This work addresses retrieval augmentation issues in Event Argument Extraction, an incremental improvement for natural language processing tasks.
The paper tackles the limitations of input length constraints and the gap between retriever and inference model in retrieval-based Event Argument Extraction (EAE) by proposing a Compressive Memory-based Retrieval (CMR) mechanism, achieving new state-of-the-art performance on three public datasets (RAMS, WikiEvents, ACE05).
Recent works have demonstrated the effectiveness of retrieval augmentation in the Event Argument Extraction (EAE) task. However, existing retrieval-based EAE methods have two main limitations: (1) input length constraints and (2) the gap between the retriever and the inference model. These issues limit the diversity and quality of the retrieved information. In this paper, we propose a Compressive Memory-based Retrieval (CMR) mechanism for EAE, which addresses the two limitations mentioned above. Our compressive memory, designed as a dynamic matrix that effectively caches retrieved information and supports continuous updates, overcomes the limitations of the input length. Additionally, after pre-loading all candidate demonstrations into the compressive memory, the model further retrieves and filters relevant information from memory based on the input query, bridging the gap between the retriever and the inference model. Extensive experiments show that our method achieves new state-of-the-art performance on three public datasets (RAMS, WikiEvents, ACE05), significantly outperforming existing retrieval-based EAE methods.