TY  - CONF
ID  - SisLab114
UR  - http://dx.doi.org/10.1109/rivf.2012.6169833
A1  - Hoang, Cuong
A1  - Le, Anh Cuong
A1  - Nguyen, Phuong Thai
A1  - Ho, Tu Bao
Y1  - 2012/03/01/
N2  - 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.
KW  - Electronic publishing;Encyclopedias;Error analysis;Internet;Length measurement;Training;language translation;learning (artificial intelligence);nonparallel corpora;parallel sentence pair detection;reinforcement learning scheme;statistical machine translation system;
TI  - Exploiting Non-Parallel Corpora for Statistical Machine Translation
SP  - 1
M2  - Ho Chi Minh city
AV  - none
EP  - 6
T2  - 2012 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF)
ER  -