@inproceedings{bhattacharyya-etal-2023-findings,
title = "Findings of the {WMT} 2023 Shared Task on Automatic Post-Editing",
author = "Bhattacharyya, Pushpak and
Chatterjee, Rajen and
Freitag, Markus and
Kanojia, Diptesh and
Negri, Matteo and
Turchi, Marco",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.55",
doi = "10.18653/v1/2023.wmt-1.55",
pages = "672--681",
abstract = "We present the results from the 9th round of the WMT shared task on MT Automatic Post-Editing, which consists of automatically correcting the output of a {``}black-box{''} machine translation system by learning from human corrections. Like last year, the task focused on English→Marathi, with data coming from multiple domains (healthcare, tourism, and general/news). Despite the consistent task framework, this year{'}s data proved to be extremely challenging. As a matter of fact, none of the official submissions from the participating teams succeeded in improving the quality of the already high-level initial translations (with baseline TER and BLEU scores of 26.6 and 70.66, respectively). Only one run, accepted as a {``}late{''} submission, achieved automatic evaluation scores that exceeded the baseline.",
}
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<abstract>We present the results from the 9th round of the WMT shared task on MT Automatic Post-Editing, which consists of automatically correcting the output of a “black-box” machine translation system by learning from human corrections. Like last year, the task focused on English→Marathi, with data coming from multiple domains (healthcare, tourism, and general/news). Despite the consistent task framework, this year’s data proved to be extremely challenging. As a matter of fact, none of the official submissions from the participating teams succeeded in improving the quality of the already high-level initial translations (with baseline TER and BLEU scores of 26.6 and 70.66, respectively). Only one run, accepted as a “late” submission, achieved automatic evaluation scores that exceeded the baseline.</abstract>
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%0 Conference Proceedings
%T Findings of the WMT 2023 Shared Task on Automatic Post-Editing
%A Bhattacharyya, Pushpak
%A Chatterjee, Rajen
%A Freitag, Markus
%A Kanojia, Diptesh
%A Negri, Matteo
%A Turchi, Marco
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F bhattacharyya-etal-2023-findings
%X We present the results from the 9th round of the WMT shared task on MT Automatic Post-Editing, which consists of automatically correcting the output of a “black-box” machine translation system by learning from human corrections. Like last year, the task focused on English→Marathi, with data coming from multiple domains (healthcare, tourism, and general/news). Despite the consistent task framework, this year’s data proved to be extremely challenging. As a matter of fact, none of the official submissions from the participating teams succeeded in improving the quality of the already high-level initial translations (with baseline TER and BLEU scores of 26.6 and 70.66, respectively). Only one run, accepted as a “late” submission, achieved automatic evaluation scores that exceeded the baseline.
%R 10.18653/v1/2023.wmt-1.55
%U https://aclanthology.org/2023.wmt-1.55
%U https://doi.org/10.18653/v1/2023.wmt-1.55
%P 672-681
Markdown (Informal)
[Findings of the WMT 2023 Shared Task on Automatic Post-Editing](https://aclanthology.org/2023.wmt-1.55) (Bhattacharyya et al., WMT 2023)
ACL