Deep Stable Learning for Cross-lingual Dependency Parsing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AUH8KHC8I" target="_blank" >RIV/00216208:11320/26:UH8KHC8I - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3735509" target="_blank" >http://dx.doi.org/10.1145/3735509</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3735509" target="_blank" >10.1145/3735509</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Stable Learning for Cross-lingual Dependency Parsing

Popis výsledku v původním jazyce

The Cross-lingual Dependency Parsing (XDP) task poses a significant challenge due to the differences in dependency structures between training and testing languages, known as the out-of-distribution (OOD) problem. Our research delved into this issue in the XDP dataset by selecting 43 languages from 22 language families. We found that the primary factor of the OOD problem is the unbalanced length distribution among languages. To address the impact of the OOD problem, we propose deep stable learning for Cross-lingual Dependency Parsing (SL-XDP), which utilizes deep stable learning with a feature fusion module. In detail, we implemented five feature fusion operations for generating comprehensive representations with dependency relations and the deep stable learning algorithm to decorrelate dependency structures with sequence length. Our experiments on Universal Dependencies have demonstrated that SL-XDP can lessen the impact of the OOD problem and improve the model generalization among 21 languages, with a maximum improvement of 18%. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Název v anglickém jazyce

Deep Stable Learning for Cross-lingual Dependency Parsing

Popis výsledku anglicky

The Cross-lingual Dependency Parsing (XDP) task poses a significant challenge due to the differences in dependency structures between training and testing languages, known as the out-of-distribution (OOD) problem. Our research delved into this issue in the XDP dataset by selecting 43 languages from 22 language families. We found that the primary factor of the OOD problem is the unbalanced length distribution among languages. To address the impact of the OOD problem, we propose deep stable learning for Cross-lingual Dependency Parsing (SL-XDP), which utilizes deep stable learning with a feature fusion module. In detail, we implemented five feature fusion operations for generating comprehensive representations with dependency relations and the deep stable learning algorithm to decorrelate dependency structures with sequence length. Our experiments on Universal Dependencies have demonstrated that SL-XDP can lessen the impact of the OOD problem and improve the model generalization among 21 languages, with a maximum improvement of 18%. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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

2025

Kód důvěrnosti údajů

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

Název periodika

ACM Transactions on Asian and Low-Resource Language Information Processing

ISSN

2375-4699

e-ISSN

—

Svazek periodika

24

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

34

Strana od-do

1-34

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105009388178