Bad MT Systems are Good for Quality Estimation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10476104" target="_blank" >RIV/00216208:11320/23:10476104 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Bad MT Systems are Good for Quality Estimation

Original language description

Quality estimation (QE) is the task of predicting quality of outputs produced by machine translation (MT) systems. Currently, the highest-performing QE systems are supervised and require training on data with golden quality scores. In this paper, we investigate the impact of the quality of the underlying MT outputs on the performance of QE systems. We find that QE models trained on datasets with lower-quality translations often outperform those trained on higher-quality data. We also demonstrate that good performance can be achieved by using a mix of data from different MT systems.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Article name in the collection

Proceedings of Machine Translation Summit XIX vol. 1: Research Track

ISBN

978-4-9913461-0-1

ISSN

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e-ISSN

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Number of pages

9

Pages from-to

200-208

Publisher name

Asia-Pacific Association for Machine Translation (AAMT)

Place of publication

Kyoto, Japan

Event location

Macau SAR, China

Event date

Sep 4, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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