| Challenge: | Automatic post-editing (APE) is an important task in natural language processing. |
| Approach: | They propose a method that explicitly models how to copy words from a machine translation to a correct translation. |
| Outcome: | The proposed method outperforms all published methods on the WMT 2016-2017 datasets. |
Similar Papers
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning (P19-1)
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| Challenge: | Existing APE systems generate artificial triplets of source sentences, machine translation outputs and human post-edits. |
| Approach: | They propose to use human post-edits to refine black-box machine translation (MT) models by fine-tuning pre-trained BERT models on both encoder and decoder of an APE system. |
| Outcome: | The proposed method improves on a dataset of 23K sentences on x86 GPUs. |
Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation (2021.eacl-main)
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| Challenge: | Automated Post-Editing (APE) aims to correct errors in the output of a given machine translation system. |
| Approach: | They propose two new methods of synthesizing additional MT outputs by adapting back-translation to the APE task, obtaining robust enlargements of existing synthetic APE training dataset. |
| Outcome: | The proposed methods improve translation quality on the English-German APE task by enlarging the existing training dataset. |
Can Automatic Post-Editing Improve NMT? (2020.emnlp-main)
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| Challenge: | APE has been successful with statistical machine translation systems but has not been as successful over neural machine translation (NMT) systems. |
| Approach: | They propose to train neural APE models on a corpus of human post-edits of NMT and compile a larger corpus to test their hypothesis. |
| Outcome: | The proposed model can improve a strong in-domain NMT system, challenging the current understanding in the field. |
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)
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| Challenge: | Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora. |
| Approach: | They propose an end-to-end deep learning framework for quality estimation and automatic post-editing of machine translation output. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the English–German dataset and human translators can significantly expedite their post-editing processing with the model. |
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach (D18-1)
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| Challenge: | Existing approaches to inducing APE have suffered from over-correction, where the APE system tends to keep the machine translated text without any modification. |
| Approach: | They propose a neural programmer-interpreter approach to automated post-editing (APE) that mimics human perform post- editing using discrete edit operations . their 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. |
| Outcome: | The proposed 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. |
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing (2022.lrec-1)
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| Challenge: | Automatic post-editing (APE) is a research field that aims to correct errors in translated sentences regardless of the utilized machine translation system. |
| Approach: | They propose a method for automatically generating APE data based on a noising scheme from a parallel corpus. |
| Outcome: | The proposed method shows that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance. |
Refer to the Reference: Reference-focused Synthetic Automatic Post-Editing Data Generation (2025.coling-main)
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| Challenge: | Existing approaches to synthetic APE data generation use source (src) sentences in a parallel corpus to obtain translations (mt) through an MT system and treat corresponding reference (ref) sentences as post-edits (pe). |
| Approach: | They propose a reference-focused synthetic APE data generation technique that uses ‘ref’ instead of src’ sentences to obtain corrupted translations. |
| Outcome: | The proposed technique improves on English-German, English-Russian, English -Marathi, English and Hindi language pairs. |
The Transference Architecture for Automatic Post-Editing (2020.coling-main)
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| Challenge: | A research challenge is the search for architectures that best support the capture, preparation and provision of src and mt information and its integration with pe decisions. |
| Approach: | They propose a multi-encoder based neural APE model that conditions post-editing decisions on both the source and machine translated text as inputs. |
| Outcome: | The proposed model outperforms the best performing systems by 1 BLEU point on the WMT 2016, 2017, and 2018 English–German APE shared tasks. |
Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages (2024.findings-emnlp)
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| Challenge: | Existing studies on multilingual automatic post-editing systems for low-resource Indo-Aryan languages have focused on different models for different language pairs. |
| Approach: | They propose to use a multilingual automatic post-editing system to improve machine translations for low-resource Indo-Aryan languages. |
| Outcome: | The proposed model outperforms English-Hindi and English-Marathi models by 2.5 and 2.39 TER points. |
LangMark: A Multilingual Dataset for Automatic Post-Editing (2025.acl-long)
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Diego Velazquez, Mikaela Grace, Konstantinos Karageorgos, Lawrence Carin, Aaron Schliem, Dimitrios Zaikis, Roger Wechsler
| Challenge: | Automated post-editing (APE) aims to correct errors in machine-translated text . lack of large-scale multilingual datasets specifically tailored to NMT outputs hinders APE development . |
| Approach: | They propose to use a human-annotated multilingual APE dataset for English translation to seven languages to address this gap. |
| Outcome: | The proposed dataset offers both linguistic diversity and scale. |