Improving linear orthogonal mapping based cross-lingual representation using ridge regression and graph centrality

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AVED7NJD9" target="_blank" >RIV/00216208:11320/25:VED7NJD9 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188573401&doi=10.1016%2fj.csl.2024.101640&partnerID=40&md5=6151af2a84f3f7facd35357c17f82d02" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188573401&doi=10.1016%2fj.csl.2024.101640&partnerID=40&md5=6151af2a84f3f7facd35357c17f82d02</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csl.2024.101640" target="_blank" >10.1016/j.csl.2024.101640</a>

Result language

angličtina

Original language name

Improving linear orthogonal mapping based cross-lingual representation using ridge regression and graph centrality

Original language description

Orthogonal linear mapping is a commonly used approach for generating cross-lingual embedding between two monolingual corpora that uses a word frequency-based seed dictionary alignment approach. While this approach is found to be effective for isomorphic language pairs, they do not perform well for distant language pairs with different sentence structures and morphological properties. For a distance language pair, the existing frequency-aligned orthogonal mapping methods suffer from two problems - (i)the frequency of source and target word are not comparable, and (ii)different word pairs in the seed dictionary may have different contribution. Motivated by the above two concerns, this paper proposes a novel centrality-aligned ridge regression-based orthogonal mapping. The proposed method uses centrality-based alignment for seed dictionary selection and ridge regression framework for incorporating influential weights of different word pairs in the seed dictionary. From various experimental observations over five language pairs (both isomorphic and distant languages), it is evident that the proposed method outperforms baseline methods in the Bilingual Dictionary Induction(BDI) task, Sentence Retrieval Task(SRT), and Machine Translation. Further, several analyses are also included to support the proposed method. © 2024 Elsevier Ltd

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

Computer Speech and Language

ISSN

0885-2308

e-ISSN

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Volume of the periodical

87

Issue of the periodical within the volume

2024

Country of publishing house

US - UNITED STATES

Number of pages

25

Pages from-to

1-25

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85188573401