@inproceedings{vu-haffari-2018-automatic,
title = "Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach",
author = "Vu, Thuy-Trang and
Haffari, Gholamreza",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1341",
doi = "10.18653/v1/D18-1341",
pages = "3048--3053",
abstract = "Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output. In this paper, we present a neural programmer-interpreter approach to this task, resembling the way that human perform post-editing using discrete edit operations, wich we refer to as programs. Our model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores.",
}
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<abstract>Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output. In this paper, we present a neural programmer-interpreter approach to this task, resembling the way that human perform post-editing using discrete edit operations, wich we refer to as programs. Our model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores.</abstract>
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%0 Conference Proceedings
%T Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach
%A Vu, Thuy-Trang
%A Haffari, Gholamreza
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct" "nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F vu-haffari-2018-automatic
%X Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output. In this paper, we present a neural programmer-interpreter approach to this task, resembling the way that human perform post-editing using discrete edit operations, wich we refer to as programs. Our model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores.
%R 10.18653/v1/D18-1341
%U https://aclanthology.org/D18-1341
%U https://doi.org/10.18653/v1/D18-1341
%P 3048-3053
Markdown (Informal)
[Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach](https://aclanthology.org/D18-1341) (Vu & Haffari, EMNLP 2018)
ACL