Zero-Shot Relation Triple Extraction with Prompts for Low-Resource Languages

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A73P4YW3V" target="_blank" >RIV/00216208:11320/23:73P4YW3V - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2076-3417/13/7/4636" target="_blank" >https://www.mdpi.com/2076-3417/13/7/4636</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app13074636" target="_blank" >10.3390/app13074636</a>

Result language

angličtina

Original language name

Zero-Shot Relation Triple Extraction with Prompts for Low-Resource Languages

Original language description

"Although low-resource relation extraction is vital in knowledge construction and characterization, more research is needed on the generalization of unknown relation types. To fill the gap in the study of low-resource (Uyghur) relation extraction methods, we created a zero-shot with a quick relation extraction task setup. Each triplet extracted from an input phrase consists of the subject, relation type, and object. This paper suggests generating structured texts by urging language models to provide related instances. Our model consists of two modules: relation generator and relation and triplet extractor. We use the Uyghur relation prompt in the relation generator stage to generate new synthetic data. In the relation and triple extraction stage, we use the new data to extract the relation triplets in the sentence. We use multi-language model prompts and structured text techniques to offer a structured relation prompt template. This method is the first research that extends relation triplet extraction to a zero-shot setting for Uyghur datasets. Experimental results show that our method achieves a maximum weighted average F1 score of 47.39%."

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

"Applied Sciences"

ISSN

2076-3417

e-ISSN

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Volume of the periodical

13

Issue of the periodical within the volume

7

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

1-16

UT code for WoS article

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EID of the result in the Scopus database

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