Automatic Post-Editing

23 papers with code • 0 benchmarks • 9 datasets

Automatic post-editing (APE) is used to correct errors in the translation made by the machine translation systems.

Most implemented papers

Levenshtein Transformer

pytorch/fairseq NeurIPS 2019

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.

Learning to Copy for Automatic Post-Editing

THUNLP-MT/THUMT IJCNLP 2019

To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way.

Felix: Flexible Text Editing Through Tagging and Insertion

google-research/google-research Findings of the Association for Computational Linguistics 2020

We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input.

Attention Strategies for Multi-Source Sequence-to-Sequence Learning

ufal/neuralmonkey 21 Apr 2017

Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities.

Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation

chrishokamp/constrained_decoding WS 2017

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 Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing Systems

ijauregiCMCRC/Shared_Attention_for_APE WS 2018

Automatic post-editing (APE) systems aim to correct the systematic errors made by machine translators.

Neural Machine Translation Techniques for Named Entity Transliteration

snukky/news-translit-nmt WS 2018

Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models.

Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach

trangvu/ape-npi EMNLP 2018

Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output.