A syntax-enhanced parameter generation network for multi-source cross-lingual event extraction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AK98723XJ" target="_blank" >RIV/00216208:11320/25:K98723XJ - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188728720&doi=10.1016%2fj.knosys.2024.111585&partnerID=40&md5=c7b86d07127aba69628a3d3c8c42070c" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188728720&doi=10.1016%2fj.knosys.2024.111585&partnerID=40&md5=c7b86d07127aba69628a3d3c8c42070c</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.knosys.2024.111585" target="_blank" >10.1016/j.knosys.2024.111585</a>
Alternative languages
Result language
angličtina
Original language name
A syntax-enhanced parameter generation network for multi-source cross-lingual event extraction
Original language description
The task of Cross-Lingual Event Extraction (CLEE) aims to transfer event knowledge from rich-resourced languages to low-resourced languages under a zero-shot cross-lingual setting. Despite presenting successful transfer abilities, existing CLEE methods face two limitations. Firstly, a majority of works concentrate on single source CLEE ignoring the benefits of multi-source potentials. Secondly, universal dependency trees have been adopted for learning shared syntactic features across languages while leaving exploration of language-specific features (e.g. word order) insufficient. In this work, we investigate the effectiveness of multi-source CLEE and propose a Syntax-enhanced Parameter Generation Network (SPGN) for the task. SPGN mainly consists of two components, i.e., a parameter generation tree network for determining language-private syntactic knowledge, and an adversarial network for learning language-shared representations. Experiments on widely used ACE2005 and recently released MINION dataset show that our proposed method significantly outperforms existing baseline systems on total 10 languages. Specifically, we observed an improvement of 1.9% in average F1-score over the best-performing baseline on ACE2005 and 3.5% on MINION, highlighting the benefits of leveraging both language-specific and shared features in CLEE. Further analysis confirms the individual effectiveness of each component of the SPGN model, suggesting its potential applicability in other low-resourced language processing tasks. © 2024 Elsevier B.V.
Czech name
—
Czech description
—
Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Knowledge-Based Systems
ISSN
0950-7051
e-ISSN
—
Volume of the periodical
292
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
—
EID of the result in the Scopus database
2-s2.0-85188728720