@inproceedings{murgolo-etal-2022-quality,
title = "A Quality Estimation and Quality Evaluation Tool for the Translation Industry",
author = "Murgolo, Elena and
Sharami, Javad Pourmostafa Roshan and
Shterionov, Dimitar",
editor = {Moniz, Helena and
Macken, Lieve and
Rufener, Andrew and
Barrault, Lo{\"\i}c and
Costa-juss{\`a}, Marta R. and
Declercq, Christophe and
Koponen, Maarit and
Kemp, Ellie and
Pilos, Spyridon and
Forcada, Mikel L. and
Scarton, Carolina and
Van den Bogaert, Joachim and
Daems, Joke and
Tezcan, Arda and
Vanroy, Bram and
Fonteyne, Margot},
booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2022",
address = "Ghent, Belgium",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2022.eamt-1.43",
pages = "307--308",
abstract = "With the increase in machine translation (MT) quality over the latest years, it has now become a common practice to integrate MT in the workflow of language service providers (LSPs) and other actors in the translation industry. With MT having a direct impact on the translation workflow, it is important not only to use high-quality MT systems, but also to understand the quality dimension so that the humans involved in the translation workflow can make informed decisions. The evaluation and monitoring of MT output quality has become one of the essential aspects of language technology management in LSPs{'} workflows. First, a general practice is to carry out human tests to evaluate MT output quality before deployment. Second, a quality estimate of the translated text, thus after deployment, can inform post editors or even represent post-editing effort. In the former case, based on the quality assessment of a candidate engine, an informed decision can be made whether the engine would be deployed for production or not. In the latter, a quality estimate of the translation output can guide the human post-editor or even make rough approximations of the post-editing effort. Quality of an MT engine can be assessed on document- or on sentence-level. A tool to jointly provide all these functionalities does not exist yet. The overall objective of the project presented in this paper is to develop an MT quality assessment (MTQA) tool that simplifies the quality assessment of MT engines, combining quality evaluation and quality estimation on document- and sentence- level.",
}
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<abstract>With the increase in machine translation (MT) quality over the latest years, it has now become a common practice to integrate MT in the workflow of language service providers (LSPs) and other actors in the translation industry. With MT having a direct impact on the translation workflow, it is important not only to use high-quality MT systems, but also to understand the quality dimension so that the humans involved in the translation workflow can make informed decisions. The evaluation and monitoring of MT output quality has become one of the essential aspects of language technology management in LSPs’ workflows. First, a general practice is to carry out human tests to evaluate MT output quality before deployment. Second, a quality estimate of the translated text, thus after deployment, can inform post editors or even represent post-editing effort. In the former case, based on the quality assessment of a candidate engine, an informed decision can be made whether the engine would be deployed for production or not. In the latter, a quality estimate of the translation output can guide the human post-editor or even make rough approximations of the post-editing effort. Quality of an MT engine can be assessed on document- or on sentence-level. A tool to jointly provide all these functionalities does not exist yet. The overall objective of the project presented in this paper is to develop an MT quality assessment (MTQA) tool that simplifies the quality assessment of MT engines, combining quality evaluation and quality estimation on document- and sentence- level.</abstract>
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%0 Conference Proceedings
%T A Quality Estimation and Quality Evaluation Tool for the Translation Industry
%A Murgolo, Elena
%A Sharami, Javad Pourmostafa Roshan
%A Shterionov, Dimitar
%Y Moniz, Helena
%Y Macken, Lieve
%Y Rufener, Andrew
%Y Barrault, Loïc
%Y Costa-jussà, Marta R.
%Y Declercq, Christophe
%Y Koponen, Maarit
%Y Kemp, Ellie
%Y Pilos, Spyridon
%Y Forcada, Mikel L.
%Y Scarton, Carolina
%Y Van den Bogaert, Joachim
%Y Daems, Joke
%Y Tezcan, Arda
%Y Vanroy, Bram
%Y Fonteyne, Margot
%S Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
%D 2022
%8 June
%I European Association for Machine Translation
%C Ghent, Belgium
%F murgolo-etal-2022-quality
%X With the increase in machine translation (MT) quality over the latest years, it has now become a common practice to integrate MT in the workflow of language service providers (LSPs) and other actors in the translation industry. With MT having a direct impact on the translation workflow, it is important not only to use high-quality MT systems, but also to understand the quality dimension so that the humans involved in the translation workflow can make informed decisions. The evaluation and monitoring of MT output quality has become one of the essential aspects of language technology management in LSPs’ workflows. First, a general practice is to carry out human tests to evaluate MT output quality before deployment. Second, a quality estimate of the translated text, thus after deployment, can inform post editors or even represent post-editing effort. In the former case, based on the quality assessment of a candidate engine, an informed decision can be made whether the engine would be deployed for production or not. In the latter, a quality estimate of the translation output can guide the human post-editor or even make rough approximations of the post-editing effort. Quality of an MT engine can be assessed on document- or on sentence-level. A tool to jointly provide all these functionalities does not exist yet. The overall objective of the project presented in this paper is to develop an MT quality assessment (MTQA) tool that simplifies the quality assessment of MT engines, combining quality evaluation and quality estimation on document- and sentence- level.
%U https://aclanthology.org/2022.eamt-1.43
%P 307-308
Markdown (Informal)
[A Quality Estimation and Quality Evaluation Tool for the Translation Industry](https://aclanthology.org/2022.eamt-1.43) (Murgolo et al., EAMT 2022)
ACL