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
DivEMT
APE
SubEdits
WMT 2016 Biomedical
WMT 2016 IT
WMT 2015 News
Latest papers with no code
Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages
This exploratory study investigates the potential of multilingual Automatic Post-Editing (APE) systems to enhance the quality of machine translations for low-resource Indo-Aryan languages.
HW-TSC's Submission to the CCMT 2024 Machine Translation Tasks
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to machine translation tasks of the 20th China Conference on Machine Translation (CCMT 2024).
MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators
To enhance the quality of error annotations predicted by LLM evaluators, we introduce a universal and training-free framework, $\textbf{MQM-APE}$, based on the idea of filtering out non-impactful errors by Automatically Post-Editing (APE) the original translation based on each error, leaving only those errors that contribute to quality improvement.
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.
Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms
Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing (APE) models due to the lack of 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.
