Multi-source domain adaptation for dependency parsing via domain-aware feature generation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AX9IFBY4B" target="_blank" >RIV/00216208:11320/25:X9IFBY4B - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202963407&doi=10.1007%2fs13042-024-02306-0&partnerID=40&md5=060923332bbaed26889271294a2824ed" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202963407&doi=10.1007%2fs13042-024-02306-0&partnerID=40&md5=060923332bbaed26889271294a2824ed</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s13042-024-02306-0" target="_blank" >10.1007/s13042-024-02306-0</a>

Result language

angličtina

Original language name

Multi-source domain adaptation for dependency parsing via domain-aware feature generation

Original language description

With deep representation learning advances, supervised dependency parsing has achieved a notable enhancement. However, when the training data is drawn from various predefined out-domains, the parsing performance drops sharply due to the domain distribution shift. The key to addressing this problem is to model the associations and differences between multiple source and target domains. In this work, we propose an innovative domain-aware adversarial and parameter generation network for multi-source cross-domain dependency parsing where a domain-aware parameter generation network is used for identifying domain-specific features and an adversarial network is used for learning domain-invariant ones. Experiments on the benchmark datasets reveal that our model outperforms strong BERT-enhanced baselines by 2 points in the average labeled attachment score (LAS). Detailed analysis of various domain representation strategies shows that our proposed distributed domain embedding can accurately capture domain relevance, which motivates the domain-aware parameter generation network to emphasize useful domain-specific representations and disregard unnecessary or even harmful ones. Additionally, extensive comparison experiments show deeper insights on the contributions of the two components. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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

2024

Confidentiality

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

Name of the periodical

International Journal of Machine Learning and Cybernetics

ISSN

1868-8071

e-ISSN

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Volume of the periodical

2024

Issue of the periodical within the volume

2024

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

1-14

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85202963407