Learning to Copy for Automatic Post-Editing

11/09/2019
by   Xuancheng Huang, et al.
0

Automatic post-editing (APE), which aims to correct errors in the output of machine translation systems in a post-processing step, is an important task in natural language processing. While recent work has achieved considerable performance gains by using neural networks, how to model the copying mechanism for APE remains a challenge. In this work, we propose a new method for modeling copying for APE. To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way. These representations are used to explicitly indicate which words in the system outputs should be copied, which is useful to help CopyNet (Gu et al., 2016) better generate post-edited translations. Experiments on the datasets of the WMT 2016-2017 APE shared tasks show that our approach outperforms all best published results.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/10/2017

Online Learning for Neural Machine Translation Post-editing

Neural machine translation has meant a revolution of the field. Neverthe...
research
06/23/2016

CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks

Neural sequence to sequence learning recently became a very promising pa...
research
09/21/2022

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

Incorporating personal preference is crucial in advanced machine transla...
research
07/01/2018

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

Automatic post-editing (APE) systems aim to correct the systematic error...
research
07/26/2017

Enforcing Constraints on Outputs with Unconstrained Inference

Increasingly, practitioners apply neural networks to complex problems in...
research
07/17/2017

LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task

This paper presents the LIG-CRIStAL submission to the shared Automatic P...
research
06/08/2020

Copy that! Editing Sequences by Copying Spans

Neural sequence-to-sequence models are finding increasing use in editing...

Please sign up or login with your details

Forgot password? Click here to reset