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A roadmap to neural automatic post-editing: an empirical approach.

Dimitar Shterionov^1,2, Félix do Carmo^3,2, Joss Moorkens⁴

¹Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands.

Machine Translation : MT

|October 5, 2020

Summary

Neural automatic post-editing (NAPE) systems improve machine translation (MT) quality. A new data augmentation method enhances NAPE performance, outperforming existing approaches in commercial translation workflows.

Keywords:

Automatic post-editing Deep learning Empirical evaluation Machine Translation Multi-source Neural post-editing

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Area of Science:

Natural Language Processing
Machine Translation
Computational Linguistics

Background:

Machine translation (MT) is typically followed by human post-editing to ensure quality.
Automatic post-editing (APE) methods reduce human effort, with neural APE (NPE) systems showing superior performance over statistical methods.
Selecting optimal NPE approaches is challenging due to numerous parameters and data dependencies.

Purpose of the Study:

To systematically analyze parameters influencing NPE performance.
To develop a decision-making roadmap for selecting NPE approaches.
To propose and evaluate a novel data augmentation method for APE.

Main Methods:

Development of an NPE decision roadmap.
Training and evaluation of 15 NPE systems with varied configurations.
Implementation and testing of a novel data augmentation technique for APE.

Main Results:

The proposed data augmentation method yielded the best-performing NPE systems.
All tested NPE systems significantly enhanced translation quality.
The study identified key parameters and decision points for effective NPE implementation.

Conclusions:

Neural automatic post-editing significantly improves translation quality in commercial workflows.
Data augmentation is a key factor in achieving state-of-the-art NPE performance.
The developed roadmap aids in selecting optimal NPE strategies for specific use cases.