relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/114/
title: Exploiting Non-Parallel Corpora for Statistical Machine Translation
creator: Hoang, Cuong
creator: Le, Anh Cuong
creator: Nguyen, Phuong Thai
creator: Ho, Tu Bao
subject: Information Technology (IT)
description: Constructing a corpus of parallel sentence pairs is an important work in building a Statistical Machine Translation system. It impacts deeply how the quality of a Statistical Machine Translation could achieve. The more parallel sentence pairs we use to train the system, the better translation's quality it is. Nowadays, comparable non-parallel corpora become important resources to alleviate scarcity of parallel corpora. The problem here is how to extract parallel sentence pairs automatically but accurately from comparable non-parallel corpora, which are usually very "noisy". This paper presents how we can apply the reinforcement-learning scheme with our new proposed algorithm for detecting parallel sentence pairs. We specify that from an initial set of parallel sentences in a domain, the proposed model can extract a large number of new parallel sentence pairs from non-parallel corpora resources in different domains, concurrently increasing the system's translation ability gradually.
date: 2012-03-01
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Hoang, Cuong and Le, Anh Cuong and Nguyen, Phuong Thai and Ho, Tu Bao  (2012) Exploiting Non-Parallel Corpora for Statistical Machine Translation.  In: 2012 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), February 27 - March 1, 2012, Ho Chi Minh city.     
relation: http://dx.doi.org/10.1109/rivf.2012.6169833