Automatic Post-Editing
25 papers with code • 0 benchmarks • 10 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
SubEdits
DivEMT
WMT 2016 IT
WMT 2016 Biomedical
WMT 2015 News
APE
Latest papers
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.
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.
Deep Copycat Networks for Text-to-Text Generation
ImperialNLP/CopyCat
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IJCNLP 2019
Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output.
Context-Aware Monolingual Repair for Neural Machine Translation
lena-voita/good-translation-wrong-in-context
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IJCNLP 2019
For training, the DocRepair model requires only monolingual document-level data in the target language.
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
deep-spin/OpenNMT-APE
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ACL 2019
Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits.
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits.
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.
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.
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.
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.
