Quality Translation Enhancement Using Sequence Knowledge and Pruning in Statistical Machine Translation

Media A. Ayu, Teddy Mantoro, Jelita Asean

Abstract


Machine translation has two important parts, a learning process which followed by a translation process. Unfortunately, most of the translation process requires complex operations and in-depth knowledge of the languages in order to give a good quality translation. This study proposes a better approach, which does not require in-depth knowledge of the linguistic properties of the languages, but it produces a good quality translation. This study evaluated 28 different parameters in IRSTLM language modeling, which resulting 270 millions experiments, and proposes a sequence evaluation mechanism based on a maximum evaluation of each parameter in producing a good quality translation based on NIST and BLEU. The parallel corpus and statistical machine learning for English and Bahasa Indonesia were used in this study. The pruning process, user interface, and the personalization of translation have a very important role in implementing of this machine translation. The result is quite promising. It shows that pruning process increases of the translation process time. The particular sequence knowledge/value parameter in translation process has a better performance than the other method using in-depth linguistic knowledge approaches. All these processes, including the process of parsing from a stand-alone mode to an online mode, are also discussed in detail.


Keywords


statistical machine translation; parallel corpus; pruning; personalization translation; hierarchical ontology; ontology matching; IRSTLM;

Full Text:

PDF


DOI: http://dx.doi.org/10.12928/telkomnika.v16i2.8687

Article Metrics

Abstract view : 157 times
PDF - 224 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604

View TELKOMNIKA Stats