In Document-level Relation Extraction (DocRE), relational facts are typically organized around a few salient entities. Explicitly capturing this topological structure is pivotal to addressing the two critical bottlenecks of the task: the extreme class imbalance and the complexity of multi-hop reasoning. Based on this insight, we first introduce the concept of the salient entity and propose a novel approach that decouples the extraction space into dense and sparse scenarios. Specifically, our approach restricts the search space for dense pairs to mitigate the dominance of the negative samples, and innovatively injects the rich semantic knowledge of salient entities to explicitly reconstruct the document for bridging disjoint evidence in multi-hop reasoning. Extensive experiments demonstrate that our approach yields consistent improvements over various backbone models and achieves advanced performance compared to existing enhancement methods.

Document-level multi-event extraction aims to identify a list of event types and corresponding arguments from the document. However, most of the current methods neglect the fine-grained difference among events in multi-event documents, which leads to event confusion and missing. This is also one of the reasons why the recall and F1-score of multi-event recognition are lower compared to single-event recognition. In this paper, we propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library, which uses a novel probe-label alignment method for differential optimization. In addition, the role contrastive loss and probe consistent loss are designed to fine-tune the fine-grained role differences and probe differences in each event. The experimental results on two general datasets show that our method outperforms the state-of-the-art method in the F1-score, especially in the recall of multi-events.

Continual few-shot relation extraction (CFRE) aims to continually learn new relations with limited samples. However, current methods neglect the instability of embeddings in the process of different task training, which leads to serious catastrophic forgetting. In this paper, we propose the concept of the following degree from the perspective of instability to analyze catastrophic forgetting and design a novel method based on adaptive gradient correction and knowledge decomposition to alleviate catastrophic forgetting. Specifically, the adaptive gradient correction algorithm is designed to limit the instability of embeddings, which adaptively constrains the current gradient to be orthogonal to the embedding space learned from previous tasks. To reduce the instability between samples and prototypes, the knowledge decomposition module decomposes knowledge into general and task-related knowledge from the perspective of model architecture, which is asynchronously optimized during training. Experimental results on two standard benchmarks show that our method outperforms the state-of-the-art CFRE model and effectively improves the following degree of embeddings.