Neural Machine Translation Quality and Post-Editing Performance

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10440593" target="_blank" >RIV/00216208:11320/21:10440593 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2021.emnlp-main.801.pdf" target="_blank" >https://aclanthology.org/2021.emnlp-main.801.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Neural Machine Translation Quality and Post-Editing Performance

Original language description

We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -&gt; Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.

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

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

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

Article name in the collection

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)

ISBN

978-1-955917-09-4

ISSN

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e-ISSN

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Number of pages

11

Pages from-to

10204-10214

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg, PA, USA

Event location

Punta Cana, Dominican Republic

Event date

Nov 7, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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