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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 1–50 of 124 papers

TitleStatusHype
A Simple and Effective Approach to Automatic Post-Editing with Transfer LearningCode1
MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation EvaluatorsCode1
Felix: Flexible Text Editing Through Tagging and InsertionCode1
Can Automatic Post-Editing Improve NMT?Code1
Context-Aware Monolingual Repair for Neural Machine TranslationCode1
Levenshtein TransformerCode1
Transfer Learning for Sequence Generation: from Single-source to Multi-sourceCode1
DynE: Dynamic Ensemble Decoding for Multi-Document SummarizationCode1
Incorporating Terminology Constraints in Automatic Post-EditingCode1
Automatic Post-Editing for VietnameseCode1
A Simple and Effective Approach to Automatic Post-Editing with Transfer LearningCode1
MLQE-PE: A Multilingual Quality Estimation and Post-Editing DatasetCode1
Automatic Correction of Human TranslationsCode1
An Empirical Study of Automatic Post-Editing—0
Adapting Neural Machine Translation for Automatic Post-Editing—0
Findings of the 2016 Conference on Machine Translation—0
An Approach Using Style Classification Features for Quality Estimation—0
Automatic Post-Editing for Machine Translation—0
Findings of the 2015 Workshop on Statistical Machine Translation—0
Findings of the 2017 Conference on Machine Translation (WMT17)—0
Automatic Post-Editing and Machine Translation Quality Estimation at eBay—0
Deepfix: Statistical Post-editing of Statistical Machine Translation Using Deep Syntactic Analysis—0
Effort-Aware Neural Automatic Post-Editing—0
A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality?—0
APE through Neural and Statistical MT with Augmented Data. ADAPT/DCU Submission to the WMT 2019 APE Shared Task—0
Are we experiencing the Golden Age of Automatic Post-Editing?—0
A Self-Supervised Automatic Post-Editing Data Generation Tool—0
BhashaVerse : Translation Ecosystem for Indian Subcontinent Languages—0
Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing—0
Alibaba Submission for WMT18 Quality Estimation Task—0
APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation—0
A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair—0
eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing—0
A Transformer-Based Multi-Source Automatic Post-Editing System—0
Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues—0
Confidence-based Rewriting of Machine Translation Output—0
A Three-Layer Architecture for Automatic Post Editing System Using Rule-Based Paradigm—0
Cross-Lingual Transformers for Neural Automatic Post-Editing—0
CUNI in WMT14: Chimera Still Awaits Bellerophon—0
CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks—0
CUNI System for WMT17 Automatic Post-Editing Task—0
Attention Strategies for Multi-Source Sequence-to-Sequence Learning—0
An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing—0
DFKI-MLT System Description for the WMT18 Automatic Post-editing Task—0
Domain Terminology Integration into Machine Translation: Leveraging Large Language Models—0
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing—0
Combining Quality Estimation and Automatic Post-editing to Enhance Machine Translation output—0
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing—0
Automatic Post-Editing for the DiscoMT Pronoun Translation Task—0
Challenges in Adaptive Neural Machine Translation—0
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