A syntax-enhanced parameter generation network for multi-source cross-lingual event extraction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A syntax-enhanced parameter generation network for multi-source cross-lingual event extraction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A syntax-enhanced parameter generation network for multi-source cross-lingual event extraction

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Knowledge-Based Systems

ISSN

0950-7051

e-ISSN

—

Svazek periodika

292

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85188728720