用动态记忆库解决事件论元抽取的输入长度与检索模型脱节问题
A Compressive Memory-based Retrieval Approach for Event Argument Extraction
- 构建可动态更新的记忆矩阵，突破输入长度限制
- 在三大数据集上达到新最优，显著超越现有方法
- 适合需要高效检索增强的事件抽取场景
近期研究证实了检索增强在事件论元抽取（EAE）任务中的有效性。然而，现有基于检索的EAE方法存在两大局限：(1) 输入长度受限；(2) 检索器与推理模型之间存在差距。这些限制制约了检索信息的多样性和质量。本文提出一种压缩记忆型检索（CMR）机制，有效解决了上述问题。我们的压缩记忆设计为可动态更新的矩阵，能高效缓存检索到的信息，克服输入长度限制。此外，在预先将所有候选示例加载至压缩记忆后，模型可根据输入查询从记忆中进一步检索并过滤相关信息，弥合了检索器与推理模型之间的鸿沟。大量实验表明，该方法在三个公开数据集（RAMS、WikiEvents、ACE05）上均取得新的最先进性能，显著优于现有基于检索的EAE方法。
原文摘要 · Abstract (English)
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.
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