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Publication Metadata only GRMT: An alternative communication tool for individuals(2013-12-01) Kanlaya Naruedomkul; Nick Cercone; Mahidol University; York Universitythe scope of translation choices of each input word, and to complete the syntax of the translation language. One set of constraints can be applied to languages, e.g., Chinese, Japanese and Thai which share common significant (syntactic) features. Our...The use of machine translation (MT) is becoming much more pervasive and a quality translation is required. Constraint application is one key feature that makes a generated translation candidate in Generate and Repair Machine Translation (GRMT) veryPublication Metadata only GRMT: An alternative communication tool for individuals(2013-07-16) Kanlaya Naruedomkul; Nick Cercone; Mahidol University; York Universityin Generate and Repair Machine Translation (GRMT) very close to a perfect translation. Constraints represent syntactic differences between the source language (SL) and the target language (TL). These constraints are used to ameliorate syntactic differences... between the SL and that of the TL, to refine the scope of translation choices of each input word, and to complete the syntax of the translation language. One set of constraints can be applied to languages, e.g., Chinese, Japanese and Thai which sharePublication Metadata only SIGNMT: An alternative sign-text MT for language learning(2013-12-01) Nadh Ditcharoen; Kanlaya Naruedomkul; Nick Cercone; Faculty of Science; Mahidol University; York UniversityWe present the SIGNMT, Thai Sign to Thai machine translation system, which is able to translate from Thai sign language into Thai text. In the translation process, SIGNMT takes into account the differences between Thai and Thai sign language... in terms of both syntax and semantic to ensure the accuracy of translation. SIGNMT was designed to be not only an automatic interpreter but also a language learning tool. It provides the meaning of each word in both text and image forms which is easyPublication Metadata only SIGNMT: An alternative sign-text MT for language learning(2013-07-16) Nadh Ditcharoena; Kanlaya Nruedomkulb; Nick Cercone; Ubon Ratchthani University; Mahidol University; York UniversityWe present the SIGNMT, Thai Sign to Thai machine translation system, which is able to translate from Thai sign language into Thai text. In the translation process, SIGNMT takes into account the differences between Thai and Thai sign language... in terms of both syntax and semantic to ensure the accuracy of translation. SIGNMT was designed to be not only an automatic interpreter but also a language learning tool. It provides the meaning of each word in both text and image forms which is easyPublication Metadata only SignMT: An alternative language learning tool(2010-08-01) Nadh Ditcharoen; Kanlaya Naruedomkul; Nick Cercone; Mahidol University; York UniversityLearning a second language is very difficult, especially, for the disabled; the disability may be a barrier to learn and to utilize information written in text form. We present the SignMT, Thai sign to Thai machine translation system, which is able... to translate from Thai sign language into Thai text. In the translation process, SignMT takes into account the differences between Thai and Thai sign language in terms of both syntax and semantic to ensure the accuracy of translation. SignMT was designedItem Metadata only On Creating an English-Thai Code-switched Machine Translation in Medical Domain(2024-01-01) Pengpun P.; Tiankanon K.; Chinkamol A.; Kinchagawat J.; Chairuengjitjaras P.; Supholkhan P.; Aussavavirojekul P.; Boonnag C.; Veerakanjana K.; Phimsiri H.; Sae-Jia B.; Sataudom N.; Ittichaiwong P.; Limkonchotiwat P.; Pengpun P.; Mahidol UniversityMachine translation (MT) in the medical domain plays a pivotal role in enhancing healthcare quality and disseminating medical knowledge. Despite advancements in English-Thai MT technology, common MT approaches often underperform in the medical field...) translation. We developed a method to produce CS medical translation data, fine-tuned a CS translation model with this data, and evaluated its performance against strong baselines, such as Google Neural Machine Translation (NMT) and GPT-3.5/GPT-4. Our model