Transferability of Syntax-Aware Graph Neural Networks in Zero-Shot Cross-Lingual Semantic Role Labeling

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ACKW6C8UD" target="_blank" >RIV/00216208:11320/25:CKW6C8UD - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217623093&partnerID=40&md5=5f7ed0d162a62107b223ccca9faac10a" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217623093&partnerID=40&md5=5f7ed0d162a62107b223ccca9faac10a</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Transferability of Syntax-Aware Graph Neural Networks in Zero-Shot Cross-Lingual Semantic Role Labeling

Original language description

Recent models in cross-lingual semantic role labeling (SRL) barely analyze the applicability of their network selection. We believe that network selection is important since it affects the transferability of cross-lingual models, i.e., how the model can extract universal features from source languages to label target languages. Therefore, we comprehensively compare the transferability of different graph neural network (GNN)-based models enriched with universal dependency trees. GNN-based models include transformer-based, graph convolutional network-based, and graph attention network (GAT)-based models. We focus our study on a zero-shot setting by training the models in English and evaluating the models in 23 target languages provided by the Universal Proposition Bank. Based on our experiments, we consistently show that syntax from universal dependency trees is essential for cross-lingual SRL models to achieve better transferability. Dependency-aware self-attention with relative position representations (SAN-RPRs) transfer best across languages, especially in the long-range dependency distance. We also show that dependency-aware two-attention relational GATs transfer better than SAN-RPRs in languages where most arguments lie in a 1-2 dependency distance. © 2024 Association for Computational Linguistics.

Czech name

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Czech description

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Type

D - Article in proceedings

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

Article name in the collection

EMNLP - Conf. Empir. Methods Nat. Lang. Process., Find. EMNLP

ISBN

979-889176168-1

ISSN

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e-ISSN

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Number of pages

23

Pages from-to

20-42

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

Miami

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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