Automatic Post-Editing
23 papers with code • 0 benchmarks • 9 datasets
Automatic post-editing (APE) is used to correct errors in the translation made by the machine translation systems.
Benchmarks
These leaderboards are used to track progress in Automatic Post-Editing
Datasets
eSCAPE
WMT 2016 News
WMT 2018 News
WMT 2015 News
APE
WMT 2016 Biomedical
WMT 2016 IT
SubEdits
Most implemented papers
Levenshtein Transformer
pytorch/fairseq
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NeurIPS 2019
We further confirm the flexibility of our model by showing a Levenshtein Transformer trained by machine translation can straightforwardly be used for automatic post-editing.
Learning to Copy for Automatic Post-Editing
THUNLP-MT/THUMT
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IJCNLP 2019
To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way.
Felix: Flexible Text Editing Through Tagging and Insertion
google-research/google-research
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Findings of the Association for Computational Linguistics 2020
We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input.
Attention Strategies for Multi-Source Sequence-to-Sequence Learning
Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities.
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation
This work presents a novel approach to Automatic Post-Editing (APE) and Word-Level Quality Estimation (QE) using ensembles of specialized Neural Machine Translation (NMT) systems.
A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing Systems
ijauregiCMCRC/Shared_Attention_for_APE
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WS 2018
Automatic post-editing (APE) systems aim to correct the systematic errors made by machine translators.
Neural Machine Translation Techniques for Named Entity Transliteration
Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models.
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach
trangvu/ape-npi
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EMNLP 2018
Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output.
