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natural-language-processing-3

Zero-shot Relation Triplet Extraction

3260 papers • 126 benchmarks • 313 datasets

Given an input sentence, the task is to extract triplets consisting of the head entity, relation label, and tail entity where the relation label is not seen at the training stage.

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Benchmarks

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FewRel

Wiki-ZSL

Libraries

Use these libraries to find zero-shot-relation-triplet-extraction-3 models and implementations

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FewRel

Wiki-ZSL

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Most implemented papers

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

Soujanya Poria, Lidong Bing, Luo Si, Yew Ken Chia•Wed Mar 16 2022

This work unify language model prompts and structured text approaches to design a structured prompt template for generating synthetic relation samples when conditioning on relation label prompts (RelationPrompt), and designs a novel Triplet Search Decoding method.

137

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Two Are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders

Wei Lu, Jue Wang•Wed Oct 07 2020

It is argued that it can be beneficial to design two distinct encoders to capture such two different types of information in the learning process, and proposed is the novel {\em table-sequence encoder} where two different encoder -- a table encoder and a sequence encoder are designed to help each other in the representation learning process.

246 0

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Zero-shot Triplet Extraction by Template Infilling

Hayate Iso, Nikita Bhutani, Estevam Hruschka, Bosung Kim, Ndapandula Nakashole•Tue Dec 20 2022

This work proposes a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling), that aligns the task objective to the pre-training objective of generative transformers to generalize to unseen relations.

15 0

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Zero-shot Relation Triplet Extraction | State-of-the-Art