Machine translation : a view from the lexicon
Machine translation applications have also been released for most mobile devices, including mobile telephones, pocket PCs, PDAs, etc. Due to their portability, such instruments have come to be designated as mobile translation tools enabling mobile business networking between partners speaking different languages, or facilitating both foreign language learning and unaccompanied traveling to foreign countries without the need of the intermediation of a human translator.
Despite being labelled as an unworthy competitor to human translation in by the Automated Language Processing Advisory Committee put together by the United States government,  the quality of machine translation has now been improved to such levels that its application in online collaboration and in the medical field are being investigated. The application of this technology in medical settings where human translators are absent is another topic of research, but difficulties arise due to the importance of accurate translations in medical diagnoses. There are many factors that affect how machine translation systems are evaluated.
These factors include the intended use of the translation, the nature of the machine translation software, and the nature of the translation process. Different programs may work well for different purposes. In certain applications, however, e. There are various means for evaluating the output quality of machine translation systems. The oldest is the use of human judges  to assess a translation's quality. Even though human evaluation is time-consuming, it is still the most reliable method to compare different systems such as rule-based and statistical systems. Relying exclusively on unedited machine translation ignores the fact that communication in human language is context-embedded and that it takes a person to comprehend the context of the original text with a reasonable degree of probability.
It is certainly true that even purely human-generated translations are prone to error. Therefore, to ensure that a machine-generated translation will be useful to a human being and that publishable-quality translation is achieved, such translations must be reviewed and edited by a human. Such research is a necessary prelude to the pre-editing necessary in order to provide input for machine-translation software such that the output will not be meaningless.
In addition to disambiguation problems, decreased accuracy can occur due to varying levels of training data for machine translating programs.
The use of lexical semantics in interlingual machine translation
Both example-based and statistical machine translation rely on a vast array of real example sentences as a base for translation, and when too many or too few sentences are analyzed accuracy is jeopardized. Researchers found that when a program is trained on , sentence pairings, accuracy actually decreases. Although there have been concerns about machine translation's accuracy, Dr.
Ana Nino of the University of Manchester has researched some of the advantages in utilizing machine translation in the classroom. One such pedagogical method is called using "MT as a Bad Model.
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Nino cites that this teaching tool was implemented in the late s. At the end of various semesters, Dr. Nino was able to obtain survey results from students who had used MT as a Bad Model as well as other models. Overwhelmingly, students felt that they had observed improved comprehension, lexical retrieval, and increased confidence in their target language. In the early s, options for machine translation between spoken and signed languages were severely limited.
It was a common belief that deaf individuals could use traditional translators. However, stress, intonation, pitch, and timing are conveyed much differently in spoken languages compared to signed languages. Therefore, a deaf individual may misinterpret or become confused about the meaning of written text that is based on a spoken language. Researchers Zhao, et al. The program would first analyze the syntactic, grammatical, and morphological aspects of the English text.
Following this step, the program accessed a sign synthesizer, which acted as a dictionary for ASL. This synthesizer housed the process one must follow to complete ASL signs, as well as the meanings of these signs. Once the entire text is analyzed and the signs necessary to complete the translation are located in the synthesizer, a computer generated human appeared and would use ASL to sign the English text to the user.
Only works that are original are subject to copyright protection, so some scholars claim that machine translation results are not entitled to copyright protection because MT does not involve creativity. From Wikipedia, the free encyclopedia. This article needs additional citations for verification.
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Main article: History of machine translation. Main article: Translation process. Main article: Rule-based machine translation. Main article: Transfer-based machine translation. Main article: Interlingual machine translation. Main article: Dictionary-based machine translation. Main article: Statistical machine translation.
Main article: Example-based machine translation. Main article: Hybrid machine translation. Main article: Neural machine translation.
ELEXR: Automatic Evaluation of Machine Translation Using Lexical Relationships | SpringerLink
Main articles: Word sense disambiguation and Syntactic disambiguation. Main article: Evaluation of machine translation. Main article: Machine translation of sign languages. Comparison of machine translation applications Statistical machine translation Controlled language in machine translation Cache language model Computational linguistics Universal Networking Language Computer-assisted translation and Translation memory Foreign language writing aid Controlled natural language Fuzzy matching Postediting History of machine translation Human language technology Humour in translation "howlers" Language and Communication Technologies Language barrier List of emerging technologies List of research laboratories for machine translation Neural machine translation Pseudo-translation Round-trip translation Translation Translation memory Universal translator Phraselator Mobile translation ULTRA machine translation system Comparison of different machine translation approaches OpenLogos.
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Retrieved 20 March Chicago, Illinois: University of Chicago Press. John Benjamins Publishing. Retrieved 13 August Elithorn and R. Banerji eds. North- Holland, pp. Association for Computational Linguistics. Retrieved 10 March In: Mitkov, Ruslan ed. Oxford: Oxford University Press. Retrieved 7 September The Possibility of Language Amsterdam:Benjamins, , 27—41 ". Reprinted in Y. Bar-Hillel: Language and information Reading, Mass.
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