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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 101–124 of 124 papers

TitleStatusHype
Alibaba Submission for WMT18 Quality Estimation Task—0
An Approach Using Style Classification Features for Quality Estimation—0
An Empirical Study of Automatic Post-Editing—0
A Neural Network based Approach to Automatic Post-Editing—0
An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing—0
APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation—0
APE through Neural and Statistical MT with Augmented Data. ADAPT/DCU Submission to the WMT 2019 APE Shared Task—0
A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality?—0
Are we experiencing the Golden Age of Automatic Post-Editing?—0
A Self-Supervised Automatic Post-Editing Data Generation Tool—0
A Three-Layer Architecture for Automatic Post Editing System Using Rule-Based Paradigm—0
A Transformer-Based Multi-Source Automatic Post-Editing System—0
Attention Strategies for Multi-Source Sequence-to-Sequence Learning—0
Automatic Post-Editing and Machine Translation Quality Estimation at eBay—0
Automatic Post-Editing for Machine Translation—0
Automatic Post-Editing for the DiscoMT Pronoun Translation Task—0
BhashaVerse : Translation Ecosystem for Indian Subcontinent Languages—0
Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing—0
CATaLog Online: A Web-based CAT Tool for Distributed Translation with Data Capture for APE and Translation Process Research—0
Challenges in Adaptive Neural Machine Translation—0
Combining Quality Estimation and Automatic Post-editing to Enhance Machine Translation output—0
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing—0
Confidence-based Rewriting of Machine Translation Output—0
Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues—0
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