Curriculum-Style Fine-Grained Adaption for Unsupervised Cross-Lingual Dependency Transfer

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ABJPD8TWC" target="_blank" >RIV/00216208:11320/23:BJPD8TWC - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144009133&doi=10.1109%2fTASLP.2022.3224302&partnerID=40&md5=c6aa01ea421e5a9756a064c117e34b9c" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144009133&doi=10.1109%2fTASLP.2022.3224302&partnerID=40&md5=c6aa01ea421e5a9756a064c117e34b9c</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/taslp.2022.3224302" target="_blank" >10.1109/taslp.2022.3224302</a>

Result language

angličtina

Original language name

Curriculum-Style Fine-Grained Adaption for Unsupervised Cross-Lingual Dependency Transfer

Original language description

"Unsupervised cross-lingual transfer has been shown great potentials for dependency parsing of the low-resource languages when there is no annotated treebank available. Recently, the self-training method has received increasing interests because of its state-of-the-art performance in this scenario. In this work, we advance the method further by coupling it with curriculum learning, which guides the self-training in an easy-to-hard manner. Concretely, we present a novel metric to measure the instance difficulty of a dependency parser which is trained mainly on a Treebank from a resource-rich source language. By using the metric, we divide a low-resource target language into several fine-grained sub-languages by their difficulties, and then apply iterative-self-training progressively on these sub-languages. To fully explore the auto-parsed training corpus from sub-languages, we exploit an improved parameter generation network to model the sub-languages for better representation learning. Experimental results show that our final curriculum-style self-training can outperform a range of strong baselines, leading to new state-of-the-art results on unsupervised cross-lingual dependency parsing. We also conduct detailed experimental analyses to examine the proposed approach in depth for comprehensive understandings. © 2014 IEEE."

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

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

2023

Confidentiality

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

Name of the periodical

"IEEE/ACM Transactions on Audio Speech and Language Processing"

ISSN

2329-9290

e-ISSN

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Volume of the periodical

31

Issue of the periodical within the volume

2023

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

322-332

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85144009133