Multi-view fusion for universal translation quality estimation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3APEVAZN2Q" target="_blank" >RIV/00216208:11320/23:PEVAZN2Q - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/summary/e0b8ef34-8e6b-412a-9b8f-87607433ed44-bb92f483/relevance/1" target="_blank" >https://www.webofscience.com/wos/woscc/summary/e0b8ef34-8e6b-412a-9b8f-87607433ed44-bb92f483/relevance/1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.inffus.2023.102022" target="_blank" >10.1016/j.inffus.2023.102022</a>

Result language

angličtina

Original language name

Multi-view fusion for universal translation quality estimation

Original language description

"Machine translation quality estimation (QE) aims to evaluate the result of translation without reference. Despite the progress it has made, state-of-the-art QE models are proven to be biased. More specifically, they over-rely on spurious statistical features while ignoring the bilingual semantic adequacy, leading to performance degradation. Besides, existing approaches require large amounts of annotation data, restricting their applications in new domains and languages. In this work, we propose a universal framework for quality estimation based on multi-view fusion. We first introduce noise to the target side of the parallel sentence pair, either by pre-trained language model or by large language model. After that, with the clean parallel pairs and the noised pairs as different views, the QE model is trained to distinguish the clean pairs from the noised ones. Our method can improve the accuracy and generalizability in supervised scenario, and can solely perform estimation in zero-shot scenario. We perform experiments on WMT QE evaluation datasets under different scenarios, verifying the effectiveness of our method. We also make an in-depth investigation of the bias of QE model."

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

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

"INFORMATION FUSION"

ISSN

1566-2535

e-ISSN

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Volume of the periodical

102

Issue of the periodical within the volume

2024-2

Country of publishing house

US - UNITED STATES

Number of pages

9

Pages from-to

1-9

UT code for WoS article

001083713100001

EID of the result in the Scopus database

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