Deep Stable Learning for Cross-lingual Dependency Parsing
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Deep Stable Learning for Cross-lingual Dependency Parsing
Original language description
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.
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
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ACM Transactions on Asian and Low-Resource Language Information Processing
ISSN
2375-4699
e-ISSN
—
Volume of the periodical
24
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
34
Pages from-to
1-34
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-105009388178