Sitthaa Phaholphinyo


Improvement of Statistical Machine Translation using Charater-Based Segmentationwith Monolingual and Bilingual Information
Vipas Sutantayawalee | Peerachet Porkaew | Prachya Boonkwan | Sitthaa Phaholphinyo | Thepchai Supnithi
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing

Character-Cluster-Based Segmentation using Monolingual and Bilingual Information for Statistical Machine Translation
Vipas Sutantayawalee | Peerachet Porkeaw | Thepchai Supnithi | Prachya Boonkwan | Sitthaa Phaholphinyo
Proceedings of the Fifth Workshop on South and Southeast Asian Natural Language Processing


Thai Word Segmentation Verification Tool
Supon Klaithin | Kanyanut Kriengket | Sitthaa Phaholphinyo | Krit Kosawat
Proceedings of the 2nd Workshop on South Southeast Asian Natural Language Processing (WSSANLP)


A Practical of Memory-based Approach for Improving Accuracy of MT
Sitthaa Phaholphinyo | Teerapong Modhiran | Nattapol Kritsuthikul | Thepchai Supnithi
Proceedings of Machine Translation Summit X: Papers

Rule-Based Machine Translation (RBMT) [1] approach is a major approach in MT research. It needs linguistic knowledge to create appropriate rules of translation. However, we cannot completely add all linguistic rules to the system because adding new rules may cause a conflict with the old ones. So, we propose a memory based approach to improve the translation quality without modifying the existing linguistic rules. This paper analyses the translation problems and shows how this approach works.