Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach

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Authors Thuy-Trang Vu, Gholamreza Haffari
Journal/Conference Name EMNLP 2018 10
Paper Category
Paper Abstract Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output. In this paper, we present a neural programmer-interpreter approach to this task, resembling the way that human perform post-editing using discrete edit operations, wich we refer to as programs. Our model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores.
Date of publication 2018
Code Programming Language Python

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