Paper Info
Title
LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task.
Abstract
This paper presents the LIG-CRIStAL submission to the shared Automatic Post-Editing task of WMT 2017. We propose two neural post-editing models: a mono-source model with a task-specific attention mechanism, which performs particularly well in a low-resource scenario; and a chained architecture which makes use of the source sentence to provide extra context. This latter architecture manages to slightly improve our results when more training data is available. We present and discuss our results on two datasets (en-de and de-en) that are made available for the task.
Year
Venue
Field
2017
empirical methods in natural language processing
Training set,Architecture,Computer science,Natural language processing,Artificial intelligence,Sentence,Machine learning
DocType
Volume
Citations 
Journal
abs/1707.05118
0
PageRank 
References 
Authors
0.34
10
3
Name
Order
Citations
PageRank
Alexandre Berard114.07
Olivier Pietquin266468.60
laurent besacier3696102.67