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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 51–100 of 124 papers

TitleStatusHype
Effort-Aware Neural Automatic Post-Editing—0
USAAR-DFKI -- The Transference Architecture for English--German Automatic Post-Editing—0
APE through Neural and Statistical MT with Augmented Data. ADAPT/DCU Submission to the WMT 2019 APE Shared Task—0
Simple Automatic Post-editing for Arabic-Japanese Machine Translation—0
A Simple and Effective Approach to Automatic Post-Editing with Transfer LearningCode1
A Simple and Effective Approach to Automatic Post-Editing with Transfer LearningCode1
Unbabel's Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing—0
Levenshtein TransformerCode1
APE at Scale and its Implications on MT Evaluation Biases—0
Alibaba Submission for WMT18 Quality Estimation Task—0
DFKI-MLT System Description for the WMT18 Automatic Post-editing Task—0
A Transformer-Based Multi-Source Automatic Post-Editing System—0
Findings of the 2018 Conference on Machine Translation (WMT18)—0
Findings of the WMT 2018 Shared Task on Automatic Post-Editing—0
Multi-source transformer with combined losses for automatic post editing—0
Multi-encoder Transformer Network for Automatic Post-Editing—0
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter ApproachCode0
MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing—0
Neural Machine Translation Techniques for Named Entity TransliterationCode0
A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing SystemsCode0
A Workbench for Rapid Generation of Cross-Lingual SummariesCode0
eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing—0
Unbabel: How to combine AI with the crowd to scale professional-quality translation—0
Automatic Post-Editing and Machine Translation Quality Estimation at eBay—0
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically motivated Test Suite—0
Combining Quality Estimation and Automatic Post-editing to Enhance Machine Translation output—0
Challenges in Adaptive Neural Machine Translation—0
A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair—0
Lightweight Word-Level Confidence Estimation for Neural Interactive Translation Prediction—0
Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing—0
Machine translation at Booking.com: what's next?—0
Are we experiencing the Golden Age of Automatic Post-Editing?—0
Japanese to English/Chinese/Korean Datasets for Translation Quality Estimation and Automatic Post-Editing—0
Neural Post-Editing Based on Quality Estimation—0
CUNI System for WMT17 Automatic Post-Editing Task—0
Findings of the 2017 Conference on Machine Translation (WMT17)—0
Generating titles for millions of browse pages on an e-Commerce site—0
LIG-CRIStAL Submission for the WMT 2017 Automatic Post-Editing Task—0
Multi-source Neural Automatic Post-Editing: FBK's participation in the WMT 2017 APE shared task—0
The AMU-UEdin Submission to the WMT 2017 Shared Task on Automatic Post-Editing—0
Unbabel's Participation in the WMT17 Translation Quality Estimation Shared Task—0
LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task—0
Attention Strategies for Multi-Source Sequence-to-Sequence Learning—0
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality EstimationCode0
An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing—0
Attention Strategies for Multi-Source Sequence-to-Sequence LearningCode0
Neural Automatic Post-Editing Using Prior Alignment and Reranking—0
Online Automatic Post-editing for MT in a Multi-Domain Translation Environment—0
Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues—0
Pushing the Limits of Translation Quality Estimation—0
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