Online machine translation

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The novelty of online machine translation services gives the impression that machine translation is relatively new. As a matter of fact, the development of Syran, a technology provider of Babel Fish, began in the 1970s. The Commission of European Communities, the developer or Syran, perceived machine translation as a cost saving device. The system provides very quick translation, usually within few minutes of submitting the text. However, due to the limitation of machine translation, Syran was never used for official translations. The system allowed its users to understand the substance of a document if they did not know the source language.

The world of the 20th century is a world of unlimited access to any source of information. The Internet created a platform for global communication. Although English is the common language used in the online dialog, some websites and information are not available in English. Furthermore, not everyone speaks English as a second language. The communication barrier is constantly preventing us from exchanging thoughts, ideas, and news. Therefore, the usage of the free online translation tools gives us an opportunity to instantly translate a text form a foreign language. Nevertheless, questions arise: how accurate is machine translation? Can we rely on it?

Language is full of exceptions and is extremely ambiguous, both at the lexical level (where the “same” word has a variety of meanings) and at the syntactic level (where a sequence of words can have different interpretations). Lets take the English word produce as an example. Produce as a verb means to make or manufacture; however, produce as a noun describes things that have been produced or grown. Humans almost unconsciously recognize the linguisitic feature, and can translate them accurately. Machines, which cannot understand the context and lack human intelligence, are incabable of performing the same job. On the other hand, we need to know that when there is a great linguistic divergence between two languages, the translation becomes more difficult. Asian languages are at a variance with English. That’s why, accordning to a study done by Chunyu Kit and Tak Ming Wong, the automatic translation from Chinese, Japanese, and Korean into English had poorer performance compared to translation between languages that come from the same family.

Presently, there are a variety of online machines translation systems. In spite of this, as we know, the quality of the translation is far from satisfactory. We cannot rely on machines when we want our message to be understood by people who do not speak our language. Machines are unable to produce quality translation, to bridge the language gap, to understand culture. However, in general, they can help when the translation quality is not a main concern, when the user wants to know the gist of the translated text, the basic idea, and later decide if the information is valuable enough to be published. If, so a human translation will be required.