Automatic Post-Editing
26 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
DivEMT
APE
SubEdits
WMT 2016 Biomedical
WMT 2016 IT
WMT 2015 News
Most implemented papers
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.
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.
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.
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.
Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings
Recent research in neural machine translation has explored flexible generation orders, as an alternative to left-to-right generation.
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
chrishokamp/dynamic-transformer-ensembles
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Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing.
Can Automatic Post-Editing Improve NMT?
To ascertain our hypothesis, we compile a larger corpus of human post-edits of English to German NMT.
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
sheffieldnlp/mlqe-pe
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LREC 2022
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE).
Incorporating Terminology Constraints in Automatic Post-Editing
zerocstaker/constrained_ape
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WMT (EMNLP) 2020
In this paper, we present both autoregressive and non-autoregressive models for lexically constrained APE, demonstrating that our approach enables preservation of 95% of the terminologies and also improves translation quality on English-German benchmarks.
Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation
Automatic Post-Editing (APE) aims to correct errors in the output of a given machine translation (MT) system.
