Zero-shot Relation Classification

5 papers with code • 2 benchmarks • 2 datasets

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Most implemented papers

RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction

declare-lab/relationprompt Findings (ACL) 2022

We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods.

Zero-shot Relation Classification from Side Information

gjiaying/zslrc 13 Nov 2020

We propose a zero-shot learning relation classification (ZSLRC) framework that improves on state-of-the-art by its ability to recognize novel relations that were not present in training data.

ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning

dinobby/ZS-BERT NAACL 2021

While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage.

Enhancing Semantic Correlation between Instances and Relations for Zero-Shot Relation Extraction

vhientran/Code-ZSRE Journal of Natural Language Processing 2023

This study argues that enhancing the semantic correlation between instances and relations is key to effectively solving the zero-shot relation extraction task.

Synthesizing Political Zero-Shot Relation Classification via Codebook Knowledge, NLI, and ChatGPT

snowood1/zero-shot-plover 15 Aug 2023

Our study underscores the efficacy of leveraging transfer learning and existing expertise to enhance research efficiency and scalability in this area.