DeepAI AI Chat
Log In Sign Up

Incremental Adaptation of NMT for Professional Post-editors: A User Study

by   Miguel Domingo, et al.
Universitat Politècnica de València

A common use of machine translation in the industry is providing initial translation hypotheses, which are later supervised and post-edited by a human expert. During this revision process, new bilingual data are continuously generated. Machine translation systems can benefit from these new data, incrementally updating the underlying models under an online learning paradigm. We conducted a user study on this scenario, for a neural machine translation system. The experimentation was carried out by professional translators, with a vast experience in machine translation post-editing. The results showed a reduction in the required amount of human effort needed when post-editing the outputs of the system, improvements in the translation quality and a positive perception of the adaptive system by the users.


page 1

page 2

page 3

page 4


A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits

The advantages of neural machine translation (NMT) have been extensively...

Online Learning for Neural Machine Translation Post-editing

Neural machine translation has meant a revolution of the field. Neverthe...

Demonstration of a Neural Machine Translation System with Online Learning for Translators

We introduce a demonstration of our system, which implements online lear...

Correct Me If You Can: Learning from Error Corrections and Markings

Sequence-to-sequence learning involves a trade-off between signal streng...

PePe: Personalized Post-editing Model utilizing User-generated Post-edits

Incorporating personal preference is crucial in advanced machine transla...

Improving CAT Tools in the Translation Workflow: New Approaches and Evaluation

This paper describes strategies to improve an existing web-based compute...

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 ...