Papers Continual Relation Extraction
“Continual Relation Extraction” 태그가 달린 논문 16편 · 필터 해제
Towards Rehearsal-Free Continual Relation Extraction: Capturing Within-Task Variance with Adaptive Prompting
Memory-based approaches have shown strong performance in Continual Relation Extraction (CRE). However, storing examples from previous tasks increases memory usage and raises privacy concerns. Recently, prompt-based metho…
Continual Relation ExtractionMixture-of-ExpertsRelationRelation Classification+1Few-shot Continual Relation Extraction via Open Information Extraction
Typically, Few-shot Continual Relation Extraction (FCRE) models must balance retaining prior knowledge while adapting to new tasks with extremely limited data. However, real-world scenarios may also involve unseen or und…
Continual LearningContinual Relation Extractiongraph constructionOpen Information Extraction+2Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
To address catastrophic forgetting in Continual Relation Extraction (CRE), many current approaches rely on memory buffers to rehearse previously learned knowledge while acquiring new tasks. Recently, prompt-based methods…
Continual Relation ExtractionMixture-of-ExpertsRelationRelation ExtractionPreserving Generalization of Language models in Few-shot Continual Relation Extraction
Few-shot Continual Relations Extraction (FCRE) is an emerging and dynamic area of study where models can sequentially integrate knowledge from new relations with limited labeled data while circumventing catastrophic forg…
Continual Relation ExtractionLanguage ModelingLanguage ModellingRelation+1A Continual Relation Extraction Approach for Knowledge Graph Completeness
Representing unstructured data in a structured form is most significant for information system management to analyze and interpret it. To do this, the unstructured data might be converted into Knowledge Graphs, by levera…
Continual Relation ExtractionKnowledge GraphsManagementnamed-entity-recognition+3DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation
Continuous Relation Extraction (CRE) aims to incrementally learn relation knowledge from a non-stationary stream of data. Since the introduction of new relational tasks can overshadow previously learned information, cata…
Continual Relation ExtractionContrastive LearningRelationRelation ExtractionRationale-Enhanced Language Models are Better Continual Relation Learners
Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the mo…
Continual Relation ExtractionRelationRelation ClassificationRelation ExtractionImproving Continual Relation Extraction by Distinguishing Analogous Semantics
Continual relation extraction (RE) aims to learn constantly emerging relations while avoiding forgetting the learned relations. Existing works store a small number of typical samples to re-train the model for alleviating…
Continual Relation ExtractionKnowledge DistillationRelationRelation ExtractionEnhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in the Wild
The principle of continual relation extraction~(CRE) involves adapting to emerging novel relations while preserving od knowledge. While current endeavors in CRE succeed in preserving old knowledge, they tend to fail when…
Continual Relation ExtractionContrastive LearningRelationRelation ExtractionEnhancing Continual Relation Extraction via Classifier Decomposition
Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. Though improvements have been shown by previous CRE studies, mo…
Continual Relation ExtractionRelationRelation ExtractionLearning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.e., the performance of old relations ser…
Continual Relation ExtractionRelationRelation ExtractionImproving Continual Relation Extraction through Prototypical Contrastive Learning
Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. In order to alleviate this…
Continual Relation ExtractionContrastive LearningRelationRelation ExtractionLess is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach
Continual relation extraction (CRE) requires the model to continually learn new relations from class-incremental data streams. In this paper, we propose a Frustratingly easy but Effective Approach (FEA) method with two l…
Continual Relation ExtractionRelation ExtractionConsistent Representation Learning for Continual Relation Extraction
Continual relation extraction (CRE) aims to continuously train a model on data with new relations while avoiding forgetting old ones. Some previous work has proved that storing a few typical samples of old relations and …
Continual Relation ExtractionContrastive LearningKnowledge DistillationRelation+2Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction
Continual learning has gained increasing attention in recent years, thanks to its biological interpretation and efficiency in many real-world applications. As a typical task of continual learning, continual relation extr…
Continual LearningContinual Relation ExtractionRelationRelation ExtractionCurriculum-Meta Learning for Order-Robust Continual Relation Extraction
Continual relation extraction is an important task that focuses on extracting new facts incrementally from unstructured text. Given the sequential arrival order of the relations, this task is prone to two serious challen…
Continual Relation ExtractionMeta-LearningRelationRelation Extraction+2