Automatic Post-Editing
25 papers with code • 0 benchmarks • 10 datasets
Automatic post-editing (APE) is used to correct errors in the translation made by the machine translation systems.
Benchmarks
These leaderboards are used to track progress in Automatic Post-Editing
Datasets
eSCAPE
WMT 2016 News
WMT 2018 News
SubEdits
DivEMT
WMT 2016 IT
WMT 2016 Biomedical
WMT 2015 News
APE
Latest papers with no code
APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation
We propose a repair-filter-use methodology that uses an APE system to correct errors on the target side of the MT training data.
Domain Terminology Integration into Machine Translation: Leveraging Large Language Models
To this end, we conduct experiments that utilise large language models (LLMs) for two purposes: generating synthetic bilingual terminology-based data, and post-editing translations generated by an MT model through incorporating pre-approved terms.
PePe: Personalized Post-editing Model utilizing User-generated Post-edits
Incorporating personal preference is crucial in advanced machine translation tasks.
An Empirical Study of Automatic Post-Editing
In view of the importance of data augmentation in APE, we separately study the impact of the construction method of artificial corpora and artificial data domain on the performance of APE models.
Towards Semi-Supervised Learning of Automatic Post-Editing: Data-Synthesis by Infilling Mask with Erroneous Tokens
Semi-supervised learning that leverages synthetic training data has been widely adopted in the field of Automatic post-editing (APE) to overcome the lack of human-annotated training data.
A Self-Supervised Automatic Post-Editing Data Generation Tool
Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences and providing suitable revisions.
Netmarble AI Center's WMT21 Automatic Post-Editing Shared Task Submission
As experimental results show, our APE system significantly improves the translations of provided MT results by -2. 848 and +3. 74 on the development dataset in terms of TER and BLEU, respectively.
Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing
This paper introduces the first corpus for Automatic Post-Editing of English and a low-resource language, Brazilian Portuguese.
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing
With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results.
NMT and PBSMT Error Analyses in English to Brazilian Portuguese Automatic Translations
In this work we present a comparative study of MT errors generated by a NMT system and a PBSMT system trained on the same English {--} Brazilian Portuguese parallel corpus.
