Zero-shot Relation Classification
6 papers with code • 2 benchmarks • 2 datasets
FewRel
Wiki-ZSL
Most implemented papers
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
declare-lab/relationprompt
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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
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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
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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
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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
Our study underscores the efficacy of leveraging transfer learning and existing expertise to enhance research efficiency and scalability in this area.
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction
Recent zero-shot classification methods converted the task to other NLP tasks (e. g., textual entailment) and used off-the-shelf models of these NLP tasks to directly perform inference on the test data without using a large amount of IE annotation data.
