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Automatic Post-Editing

Automatic post-editing (APE) is used to correct errors in the translation made by the machine translation systems.

Papers

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Showing 1–25 of 124 papers

TitleStatusHype
MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation EvaluatorsCode1
Automatic Correction of Human TranslationsCode1
Transfer Learning for Sequence Generation: from Single-source to Multi-sourceCode1
Automatic Post-Editing for VietnameseCode1
Incorporating Terminology Constraints in Automatic Post-EditingCode1
MLQE-PE: A Multilingual Quality Estimation and Post-Editing DatasetCode1
Can Automatic Post-Editing Improve NMT?Code1
DynE: Dynamic Ensemble Decoding for Multi-Document SummarizationCode1
Felix: Flexible Text Editing Through Tagging and InsertionCode1
Context-Aware Monolingual Repair for Neural Machine TranslationCode1
A Simple and Effective Approach to Automatic Post-Editing with Transfer LearningCode1
A Simple and Effective Approach to Automatic Post-Editing with Transfer LearningCode1
Levenshtein TransformerCode1
Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained Decoding for Automatic Post-Editing—0
BhashaVerse : Translation Ecosystem for Indian Subcontinent Languages—0
Together We Can: Multilingual Automatic Post-Editing for Low-Resource LanguagesCode0
HW-TSC's Submission to the CCMT 2024 Machine Translation Tasks—0
APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation—0
Domain Terminology Integration into Machine Translation: Leveraging Large Language Models—0
TMU NMT System with Automatic Post-Editing by Multi-Source Levenshtein Transformer for the Restricted Translation Task of WAT 2022—0
PePe: Personalized Post-editing Model utilizing User-generated Post-edits—0
An Empirical Study of Automatic Post-Editing—0
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing—0
Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms—0
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative StudyCode0
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