@inproceedings{bittlingmayer-etal-2022-quality,
title = "Quality Prediction",
author = "Bittlingmayer, Adam and
Zubarev, Boris and
Aleksanyan, Artur",
editor = "Campbell, Janice and
Larocca, Stephen and
Marciano, Jay and
Savenkov, Konstantin and
Yanishevsky, Alex",
booktitle = "Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)",
month = sep,
year = "2022",
address = "Orlando, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2022.amta-upg.12",
pages = "159--180",
abstract = "A growing share of machine translations are approved - untouched - by human translators in post-editing workflows. But they still cost time and money. Now companies are getting human post-editing quality faster and cheaper, by automatically approving the good machine translations - at human accuracy. The approach has evolved, from research papers on machine translation quality estimation, to adoption inside companies like Amazon, Facebook, Microsoft and VMWare, to self-serve cloud APIs like ModelFront. We{'}ll walk through the motivations, use cases, prerequisites, adopters, providers, integration and ROI.",
}
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<abstract>A growing share of machine translations are approved - untouched - by human translators in post-editing workflows. But they still cost time and money. Now companies are getting human post-editing quality faster and cheaper, by automatically approving the good machine translations - at human accuracy. The approach has evolved, from research papers on machine translation quality estimation, to adoption inside companies like Amazon, Facebook, Microsoft and VMWare, to self-serve cloud APIs like ModelFront. We’ll walk through the motivations, use cases, prerequisites, adopters, providers, integration and ROI.</abstract>
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%0 Conference Proceedings
%T Quality Prediction
%A Bittlingmayer, Adam
%A Zubarev, Boris
%A Aleksanyan, Artur
%Y Campbell, Janice
%Y Larocca, Stephen
%Y Marciano, Jay
%Y Savenkov, Konstantin
%Y Yanishevsky, Alex
%S Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)
%D 2022
%8 September
%I Association for Machine Translation in the Americas
%C Orlando, USA
%F bittlingmayer-etal-2022-quality
%X A growing share of machine translations are approved - untouched - by human translators in post-editing workflows. But they still cost time and money. Now companies are getting human post-editing quality faster and cheaper, by automatically approving the good machine translations - at human accuracy. The approach has evolved, from research papers on machine translation quality estimation, to adoption inside companies like Amazon, Facebook, Microsoft and VMWare, to self-serve cloud APIs like ModelFront. We’ll walk through the motivations, use cases, prerequisites, adopters, providers, integration and ROI.
%U https://aclanthology.org/2022.amta-upg.12
%P 159-180
Markdown (Informal)
[Quality Prediction](https://aclanthology.org/2022.amta-upg.12) (Bittlingmayer et al., AMTA 2022)
ACL
- Adam Bittlingmayer, Boris Zubarev, and Artur Aleksanyan. 2022. Quality Prediction. In Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track), pages 159–180, Orlando, USA. Association for Machine Translation in the Americas.