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Found 9 papers, 2 papers with code
Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output
For helpful quality levels, a visualization reflecting the uncertainty of the QE model is preferred.
Mid-Air Hand Gestures for Post-Editing of Machine Translation
Here, we present the first study that investigates the usefulness of mid-air hand gestures in combination with the keyboard (GK) for text editing in PE of MT.
MMPE: A Multi-Modal Interface for Post-Editing Machine Translation
On the other hand, speech and multi-modal combinations of select {\&} speech are considered suitable for replacements and insertions but offer less potential for deletion and reordering.
MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering, and Speech Commands for Post-Editing Machine Translation
The shift from traditional translation to post-editing (PE) of machine-translated (MT) text can save time and reduce errors, but it also affects the design of translation interfaces, as the task changes from mainly generating text to correcting errors within otherwise helpful translation proposals.
The Transference Architecture for Automatic Post-Editing
In automatic post-editing (APE) it makes sense to condition post-editing (pe) decisions on both the source (src) and the machine translated text (mt) as input.
USAAR-DFKI -- The Transference Architecture for English--German Automatic Post-Editing
In this paper we present an English{--}German Automatic Post-Editing (APE) system called transference, submitted to the APE Task organized at WMT 2019.
Integrating Artificial and Human Intelligence for Efficient Translation
Current advances in machine translation increase the need for translators to switch from traditional translation to post-editing of machine-translated text, a process that saves time and improves quality.
A Transformer-Based Multi-Source Automatic Post-Editing System
The proposed model is an extension of the transformer architecture: two separate self-attention-based encoders encode the machine translation output (mt) and the source (src), followed by a joint encoder that attends over a combination of these two encoded sequences (encsrc and encmt) for generating the post-edited sentence.
