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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

Showing 150 of 124 papers

TitleStatusHype
Automatic Post-Editing for VietnameseCode1
Incorporating Terminology Constraints in Automatic Post-EditingCode1
DynE: Dynamic Ensemble Decoding for Multi-Document SummarizationCode1
Levenshtein TransformerCode1
MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation EvaluatorsCode1
A Simple and Effective Approach to Automatic Post-Editing with Transfer LearningCode1
Felix: Flexible Text Editing Through Tagging and InsertionCode1
Context-Aware Monolingual Repair for Neural Machine TranslationCode1
Transfer Learning for Sequence Generation: from Single-source to Multi-sourceCode1
Automatic Correction of Human TranslationsCode1
MLQE-PE: A Multilingual Quality Estimation and Post-Editing DatasetCode1
A Simple and Effective Approach to Automatic Post-Editing with Transfer LearningCode1
Can Automatic Post-Editing Improve NMT?Code1
A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing SystemsCode0
A Workbench for Rapid Generation of Cross-Lingual SummariesCode0
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter ApproachCode0
Deep Copycat Networks for Text-to-Text GenerationCode0
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality EstimationCode0
Attention Strategies for Multi-Source Sequence-to-Sequence LearningCode0
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative StudyCode0
Neural Machine Translation Techniques for Named Entity TransliterationCode0
Learning to Copy for Automatic Post-EditingCode0
Phrasal Substitution of Idiomatic ExpressionsCode0
Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data GenerationCode0
Exploring the Importance of Source Text in Automatic Post-Editing for Context-Aware Machine TranslationCode0
Learning Non-Monotonic Automatic Post-Editing of Translations from Human OrderingsCode0
Together We Can: Multilingual Automatic Post-Editing for Low-Resource LanguagesCode0
Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing0
BhashaVerse : Translation Ecosystem for Indian Subcontinent Languages0
A Self-Supervised Automatic Post-Editing Data Generation Tool0
An Empirical Study of Automatic Post-Editing0
Are we experiencing the Golden Age of Automatic Post-Editing?0
Automatic Post-Editing for the DiscoMT Pronoun Translation Task0
Automatic Post-Editing for Machine Translation0
A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality?0
An Approach Using Style Classification Features for Quality Estimation0
Adapting Neural Machine Translation for Automatic Post-Editing0
Automatic Post-Editing and Machine Translation Quality Estimation at eBay0
Deepfix: Statistical Post-editing of Statistical Machine Translation Using Deep Syntactic Analysis0
APE through Neural and Statistical MT with Augmented Data. ADAPT/DCU Submission to the WMT 2019 APE Shared Task0
CUNI System for WMT17 Automatic Post-Editing Task0
Attention Strategies for Multi-Source Sequence-to-Sequence Learning0
CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks0
DFKI-MLT System Description for the WMT18 Automatic Post-editing Task0
Domain Terminology Integration into Machine Translation: Leveraging Large Language Models0
CUNI in WMT14: Chimera Still Awaits Bellerophon0
Effort-Aware Neural Automatic Post-Editing0
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing0
APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation0
Alibaba Submission for WMT18 Quality Estimation Task0
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