Benchmarks
Datasets
Greatest papers with code
Levenshtein Transformer
NeurIPS 2019
• pytorch/fairseq
• 
We further confirm the flexibility of our model by showing a Levenshtein Transformer trained by machine translation can straightforwardly be used for automatic post-editing.
Ranked #4 on
Machine Translation
on WMT2016 Romanian-English
AUTOMATIC POST-EDITING MACHINE TRANSLATION TEXT SUMMARIZATION
Learning to Copy for Automatic Post-Editing
IJCNLP 2019
• THUNLP-MT/THUMT
• 
To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way.
Context-Aware Monolingual Repair for Neural Machine Translation
IJCNLP 2019
• lena-voita/good-translation-wrong-in-context
•
For training, the DocRepair model requires only monolingual document-level data in the target language.
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation
This work presents a novel approach to Automatic Post-Editing (APE) and Word-Level Quality Estimation (QE) using ensembles of specialized Neural Machine Translation (NMT) systems.
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
ACL 2019
• deep-spin/OpenNMT-APE
•
Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits.
AUTOMATIC POST-EDITING MACHINE TRANSLATION TRANSFER LEARNING
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits.
AUTOMATIC POST-EDITING MACHINE TRANSLATION TRANSFER LEARNING
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
• chrishokamp/dynamic-transformer-ensembles
•
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing.
AUTOMATIC POST-EDITING DOCUMENT SUMMARIZATION MACHINE TRANSLATION MULTI-DOCUMENT SUMMARIZATION
Can Automatic Post-Editing Improve NMT?
To ascertain our hypothesis, we compile a larger corpus of human post-edits of English to German NMT.
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE).
Deep Copycat Networks for Text-to-Text Generation
IJCNLP 2019
• ImperialNLP/CopyCat
•
Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output.
AUTOMATIC POST-EDITING MACHINE TRANSLATION TEXT GENERATION TEXT SIMPLIFICATION