Zero-shot Relation Triplet Extraction

3 papers with code • 2 benchmarks • 2 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.

Most implemented papers

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

LorrinWWW/two-are-better-than-one EMNLP 2020

In this work, we propose the novel {\em table-sequence encoders} where two different encoders -- a table encoder and a sequence encoder are designed to help each other in the representation learning process.

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 Triplet Extraction by Template Infilling

megagonlabs/zett 21 Dec 2022

We propose 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.