Paper Info
Title
Machine Translation Model based on Non-parallel Corpus and Semi-supervised Transductive Learning.
Abstract
Although the parallel corpus has an irreplaceable role in machine translation, its scale and coverage is still beyond the actual needs. Non-parallel corpus resources on the web have an inestimable potential value in machine translation and other natural language processing tasks. This article proposes a semi-supervised transductive learning method for expanding the training corpus in statistical machine translation system by extracting parallel sentences from the non-parallel corpus. This method only requires a small amount of labeled corpus and a large unlabeled corpus to build a high-performance classifier, especially for when there is short of labeled corpus. The experimental results show that by combining the non-parallel corpus alignment and the semi-supervised transductive learning method, we can more effectively use their respective strengths to improve the performance of machine translation system.
Year
Venue
Field
2014
arXiv: Computation and Language
Transduction (machine learning),Example-based machine translation,Semi-supervised learning,Computer science,Machine translation system,Machine translation,Natural language processing,Artificial intelligence,Classifier (linguistics),Machine learning
DocType
Volume
Citations 
Journal
abs/1405.5654
0
PageRank 
References 
Authors
0.34
6
1
Name
Order
Citations
PageRank
Lijiang Chen130423.22