MT4CrossOIE: Multi-stage tuning for cross-lingual open information extraction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AG2EURCKF" target="_blank" >RIV/00216208:11320/25:G2EURCKF - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199005546&doi=10.1016%2fj.eswa.2024.124760&partnerID=40&md5=5346c2848fb28ffc346f43d5691c9ab9" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199005546&doi=10.1016%2fj.eswa.2024.124760&partnerID=40&md5=5346c2848fb28ffc346f43d5691c9ab9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2024.124760" target="_blank" >10.1016/j.eswa.2024.124760</a>

Result language

angličtina

Original language name

MT4CrossOIE: Multi-stage tuning for cross-lingual open information extraction

Original language description

Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages. Previous work uses a shared cross-lingual pre-trained model to handle the different languages but underuses the potential of the language-specific representation. In this paper, we propose an effective multi-stage tuning framework called MT4CrossOIE, designed for enhancing cross-lingual open information extraction by injecting language-specific knowledge into the shared model. Specifically, the cross-lingual pre-trained model is first tuned in a shared semantic space (e.g., embedding matrix) in the fixed encoder and then other components are optimized in the second stage. After enough training, we freeze the pre-trained model and tune the multiple extra low-rank language-specific modules using mixture of LoRAs for model-based cross-lingual transfer. In addition, we leverage two-stage prompting to encourage the large language model (LLM) to annotate the multi-lingual raw data for data-based cross-lingual transfer. The model is trained with multi-lingual objectives on our proposed dataset OpenIE4++ by combining the model-based and data-based transfer techniques. Experimental results on various benchmarks emphasize the importance of aggregating multiple plug-in-and-play language-specific modules and demonstrate the effectiveness of MT4CrossOIE in cross-lingual OIE. © 2024

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

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

2024

Confidentiality

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

Name of the periodical

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

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Volume of the periodical

255

Issue of the periodical within the volume

2024

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

1-12

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85199005546