Papers Few-Shot Relation Classification
“Few-Shot Relation Classification” 태그가 달린 논문 23편 · 필터 해제
Diversity Over Quantity: A Lesson From Few Shot Relation Classification
In few-shot relation classification (FSRC), models must generalize to novel relations with only a few labeled examples. While much of the recent progress in NLP has focused on scaling data size, we argue that diversity i…
DiversityFew-Shot LearningFew-Shot Relation ClassificationRelation+1Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. It is generally formulated as a task to recognize the relationship between two entities of interest…
Feature EngineeringFew-Shot LearningFew-Shot Relation ClassificationKnowledge Graph Completion+5Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
Differentiating relationships between entity pairs with limited labeled instances poses a significant challenge in few-shot relation classification. Representations of textual data extract rich information spanning the d…
ClassificationContrastive LearningFew-Shot Relation ClassificationRelation+3Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification
This paper introduces a novel neuro-symbolic architecture for relation classification (RC) that combines rule-based methods with contemporary deep learning techniques. This approach capitalizes on the strengths of both p…
Few-Shot Relation ClassificationRelationRelation ClassificationSemantic Text Matching+1CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities. CORE includes 4,708 instances of 12 relation types with corresponding textual evidence extracted …
Domain AdaptationFew-Shot Relation ClassificationRelationRelation Classification+1Dependency-aware Prototype Learning for Few-shot Relation Classification
Few-shot relation classification aims to classify the relation type between two given entities in a sentence by training with a few labeled instances for each relation. However, most of existing models fail to distinguis…
ClassificationFew-Shot Relation ClassificationRelationRelation Classification+1Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text
Few-shot relation classification is difficult because the few instances available may not represent well the relation patterns. Some existing approaches explored extra information such as relation definition, in addition…
Few-Shot Relation ClassificationRelationRelation ClassificationRepresentation LearningFew-Shot Document-Level Relation Extraction
We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora pr…
Document-level Relation ExtractionDomain AdaptationFew-Shot LearningFew-Shot Relation Classification+4Cross Domain Few-Shot Learning via Meta Adversarial Training
Few-shot relation classification (RC) is one of the critical problems in machine learning. Current research merely focuses on the set-ups that both training and testing are from the same domain. However, in practice, thi…
Cross-Domain Few-Shotcross-domain few-shot learningFew-Shot LearningFew-Shot Relation Classification+1Towards Realistic Few-Shot Relation Extraction
In recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpa…
ClassificationFew-Shot LearningFew-Shot Relation ClassificationRelation+2Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning
Standard few-shot relation classification (RC) is designed to learn a robust classifier with only few labeled data for each class. However, previous works rarely investigate the effects of a different number of classes (…
Contrastive LearningFew-Shot LearningFew-Shot Relation ClassificationRelation+1From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification
“Few-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification. Most previous works onfew-shot relation classifica…
ClassificationFew-Shot Relation ClassificationRelationRelation ClassificationRevisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes
We explore Few-Shot Learning (FSL) for Relation Classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, aka NOTA), we …
ClassificationFew-Shot LearningFew-Shot Relation ClassificationGeneral Classification+2Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification
Relation classification (RC) task is one of fundamental tasks of information extraction, aiming to detect the relation information between entity pairs in unstructured natural language text and generate structured data i…
Few-Shot Relation ClassificationRelationRelation ClassificationRepresentation LearningMeta-Information Guided Meta-Learning for Few-Shot Relation Classification
Few-shot classification requires classifiers to adapt to new classes with only a few training instances. State-of-the-art meta-learning approaches such as MAML learn how to initialize and fast adapt parameters from limit…
ClassificationFew-Shot Relation ClassificationMeta-LearningRelation+1A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification
Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emerge…
Few-Shot LearningFew-Shot Relation ClassificationLifelong learningRelation+2Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep high co-occurrence in the same context, …
Few-Shot Relation ClassificationRelationRelation ClassificationSentence小样本关系分类研究综述(Few-Shot Relation Classification: A Survey)
关系分类作为构建结构化知识的重要一环,在自然语言处理领域备受关注。但在很多应用领域中(医疗、金融领域),收集充足的用于训练关系分类模型的数据是十分困难的。近年来,仅需要少量训练样本的小样本学习研究逐渐新兴于各大领域。本文对近期小样本关系分类模型与方法进行了系统的综述。根据度量方法的不同,将现有方法分为原型式和分布式两大类。根据是否利用额外信息,将模型分为预训练和非预训练两大类。此外,除了常规设定下的小样本学习,本文还梳理了跨领域和稀缺资…
Few-Shot Relation ClassificationRelation ClassificationMICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data
Few-shot relation classification seeks to classify incoming query instances after meeting only few support instances. This ability is gained by training with large amount of in-domain annotated data. In this paper, we ta…
ClassificationFew-Shot LearningFew-Shot Relation ClassificationGeneral Classification+3FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? (2) Can they detect none-of-the-a…
ClassificationDomain AdaptationFew-Shot Relation ClassificationGeneral Classification+2