Machine Translation – Overview
What is Machine Translation
The term machine translation (MT) is used in the sense of translation of one language to another. The ideal aim of MT systems is to produce the best possible translation without human assistance. Basically every MT system requires programs for translation and automated dictionaries and grammars to support translation.
Types Of Machine Translation Systems
MT systems that produce translations between only two particular languages are called bilingual systems and those that produce translations for any given pair of languages are called multilingual systems. Multilingual systems may be either uni-directional or bi-directional. Multilingual systems are preferred to be bi-directional and bi-lingual as they have ability to translate from any given language to any other given language and vice versa.
Direct Machine Translation Approach
Direct translation approach is the oldest and less popular approach. MT systems that use this approach are capable of translating a language, called source language (SL) directly to another language, called target language (TL). The analysis of SL texts is oriented to only one TL. Direct translation systems are basically bilingual and uni-directional.
Interlingua Approach
Interlingua approach intents to translate SL texts to that of more than one language. Translation is from SL to an intermediate form called interlingua (IL) and then from IL to TL. Interlingua may be artificial one or auxiliary language like Esperanto with universal vocabulary.
Transfer Approach
Unlike interlingua approach, transfer approach has three stages involved. In the first stage, SL texts are converted into abstract SL-oriented representations. In the second stage, SL-oriented representations are converted into equivalent TL-oriented representations. Final texts are generated in the third stage.
Empirical Machine Translation Approach
Empirical approach is the emerging one that uses large amount of raw data in the form of parallel corpora. The raw data consists of texts and their translations. Example-based MT, analogy-based MT, memory-based MT, and case-based MT are the techniques that use empirical approach. Basically all these techniques use a corpus or database of translated examples. Statistical MT is corpus based but slightly different in the sense that it depends on statistical modelling of the word order of the target language and of source-target word equivalences. Statistical MT automatically learns lexical and structural preferences from corpora. Statistical models offer good solution to ambiguity problem. They are robust and work well even if there are errors and the presence of new data.
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