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.
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Machine Translation
on WMT2016 Romanian-English
AUTOMATIC POST-EDITING MACHINE TRANSLATION TEXT SUMMARIZATION
To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way.
For training, the DocRepair model requires only monolingual document-level data in the target language.
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.
Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits.
AUTOMATIC POST-EDITING MACHINE TRANSLATION TRANSFER LEARNING
Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits.
AUTOMATIC POST-EDITING MACHINE TRANSLATION TRANSFER LEARNING
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing.
AUTOMATIC POST-EDITING DOCUMENT SUMMARIZATION MACHINE TRANSLATION MULTI-DOCUMENT SUMMARIZATION
To ascertain our hypothesis, we compile a larger corpus of human post-edits of English to German NMT.
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE).
Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output.
AUTOMATIC POST-EDITING MACHINE TRANSLATION TEXT GENERATION TEXT SIMPLIFICATION