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 | Artificial Intelligence |
| 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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