Challenge: Existing methods to predict relationships with given entity pairs are lacking in supervised methods.
Approach: They propose a framework for zero-shot Relation Extraction that includes two modules: Custom Embedding and Dynamic Aggregation.
Outcome: The proposed framework shows competitive performance on two ZSRE datasets.

Similar Papers

AlignRE: An Encoding and Semantic Alignment Approach for Zero-Shot Relation Extraction (2024.findings-acl)

Copied to clipboard

Challenge: Existing prototype-based methods for ZSRE ignore abundant side information and suffer from a significant encoding gap between prototypes and sentences.
Approach: They propose a framework to encode schema alignment to enhance prototype-based ZSRE methods.
Outcome: The proposed method outperforms existing methods on FewRel and Wiki-ZSL datasets and exhibits substantially faster performance and reduces the need for extensive manual labor in prototype construction.
From Local Perspective to Global Reasoning: A Neuro-Symbolic Framework for Zero-Shot Relation Extraction (2026.findings-acl)

Copied to clipboard

Challenge: Existing methods for zero-shot relationship extraction do not distinguish between unseen, semantically similar relations.
Approach: They propose a framework to enable global reasoning across a set of predictions.
Outcome: The proposed framework outperforms existing methods and establishes new state-of-the-art results on widely used datasets.
Entity Concept-enhanced Few-shot Relation Extraction (2021.acl-short)

Copied to clipboard

Challenge: Existing FSRE methods fail to classify relations based on information of sentences and entity pairs due to limited samples and lack of knowledge.
Approach: They propose a concept-sentence attention module to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts.
Outcome: The proposed scheme outperforms existing methods on a few-shot relation extraction dataset.
Document-Level Zero-Shot Relation Extraction with Entity Side Information (2026.eacl-long)

Copied to clipboard

Challenge: Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels.
Approach: They propose a document-level zero-shot relation extraction framework with Entity Side Information to solve existing problems.
Outcome: The proposed approach achieves an average improvement of 11.6% in the macro F1-Score compared to baseline models and existing benchmarks.
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning (2021.naacl-main)

Copied to clipboard

Challenge: Existing methods to relation extraction require labeled data, but labeling is difficult . Existing models cannot recognize rare instances that are never covered by training data .
Approach: They propose a multi-task learning model that directly predicts unseen relations without hand-crafted attribute labeling and multiple pairwise classifications.
Outcome: The proposed model outperforms existing methods by 13.54% on two well-known datasets.
HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction (2023.emnlp-main)

Copied to clipboard

Challenge: Existing studies cannot generalize well to unseen relations using Prototypical Networks . current approaches are dependent on large amount of labeled data and cannot deal with unseense relations well.
Approach: They propose a HyperNetwork-based Decoupling approach to improve FSRE generalization . they propose FSre models with an encoder, network generator and refined classifiers .
Outcome: The proposed method improves the generalization of few-shot relation extraction models.
Re-Cent: A Relation-Centric Framework for Joint Zero-Shot Relation Triplet Extraction (2025.coling-main)

Copied to clipboard

Challenge: Existing methods to extract triplets from context often decompose into named entity recognition and relation classification, which may introduce error propagation.
Approach: They propose a Relation-centric joint ZSRTE method which leverages unseen relation labels to extract triplets in one go.
Outcome: The proposed method achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor.
Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction (2025.findings-emnlp)

Copied to clipboard

Challenge: Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data.
Approach: They propose a plug-in retrieval adjuster that allows rapid fine-tuning without accessing LLMs’ parameters.
Outcome: The proposed model demonstrates comparable performance on multiple benchmarks.
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction (2020.coling-main)

Copied to clipboard

Challenge: Existing approaches to supervised relational triple extraction require huge amounts of labeled data.
Approach: They propose a multi-prototype embedding network model to extract the composition of relational triples from unstructured text.
Outcome: The proposed method improves the performance of the few-shot relational triple extraction problem.
GRADUAL: Granularity-aware Dual Prototype Learning for Better Few-Shot Relation Extraction (2024.findings-acl)

Copied to clipboard

Challenge: Existing methods for few-shot relation extraction use text labels and context sentences to learn prototype representations.
Approach: They propose a "dual prototype learning method" that integrates text labels and context sentences into prototype representations.
Outcome: The proposed method achieves state-of-the-art performance in few-shot relation extraction.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations