An Overview Of Machine Translation
Introduction
The idea of translation is comes from the lingustic diversity. It is not possible to know and grasp all the languages within the world by human beings. Around 5000 languages present in the world that shows the need of language translation methods. We are living in growingly linked world: market, countries and populations. Translation connects people and business across the global and allow them to communicate in any language.
Machine translation is revolutionary technology which that transform the field of translation. It is a sub-field of computational linguistics that investigates the use of software to translate text or speech from a one source language to another target language. just like that translate the Hindi phrase into English phrase. Machine Translation is Sometime referred by the abbrviation MT .In this translation of text from one language to another, there is no human involvement and it is the machine which performs the process of conversion. Machine Translation methods are different and each has its own benefits and drawback.
Methods of Machine Translation
Machine translation can use a method based on linguistic rules, which means that words will be translated in a linguistic way — the most suitable (orally speaking) words of the target language will replace the ones in the source language.
Bernard Vauquois’ pyramid showing comparative depths of intermediary representation, interlingual machine translation at the peak, followed by transfer-based, then direct translation.
There are five types of machine translation– Rule-based Machine Translation (RBMT),Statistical Machine Translation (SMT),Example-based, Hybrid Machine Translation, and Neural Machine Translation.
1)Rule-based machine translation(RBMT):-
RBMT is classical Approach of MT. It is machine translation systems based on linguistic information about source and target languages basically retrieved from (unilingual, bilingual or multilingual) dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively. The rule-based machine translation paradigm includes transfer-based machine translation, interlingual machine translation and dictionary-based machine translation paradigms.
i)Transfer-based machine translation
Transfer-based machine translation is similar to interlingual machine translation in that it creates a translation from an intermediate representation that simulates the meaning of the original sentence. Unlike interlingual MT, it depends partially on the language pair involved in the translation.
ii)Interlingual machine translation
Interlingual machine translation is one of the classic approaches to machine translation. In this approach, the source language, i.e. the text to be translated is transformed into an interlingua, i.e., an abstract language-independent representation. The target language is then generated from the interlingua.
iii)Dictionary-based machine translation
Machine translation can use a method based on dictionary entries, which means that the words will be translated as a dictionary does — word by word, usually without much correlation of meaning between them.
2)Statistical Machine Translation (SMT):-
Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation. It aims to determine the correspondence between a word from the source language and a word from the target language. A good example of this is Google Translate. SMT is great for basic translation, but its greatest drawback is that it does not factor in context, which means translations can often be erroneous. In other words, don’t expect high-quality translations.
3) Example-based machine translation:-
Example-based machine translation (EBMT) is a method of machine translation often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base at run-time. It is essentially a translation by analogy and can be viewed as an implementation of a case-based reasoning approach to machine learning. In this approach, the corpus that is used is one that contains texts that have already been translated.
4) Hybrid machine translation:-
Hybrid machine translation is a method of machine translation that is characterized by the use of multiple machine translation approaches within a single machine translation system. The motivation for developing hybrid machine translation systems stems from the failure of any single technique to achieve a satisfactory level of accuracy.
5)Neural machine translation:-
Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
Benefits
when Machine Translation is done without human intervention, is best suited for relatively simple text that has low visibility. Traditionally, it has been implemented for user-generated content such as reviews, forums and auctions, like eBay. Depending on your quality expectations, content type and purpose, Machine Translation could do a decent job at translating simple, general business documents in some languages.
Here is the main perks to use MT:
1)Saves time:
Machine language translation can save significant time as it is capable of translating entire text documents in seconds.
2)Reduces costs:
Machine Translation can substantially lower your costs, as it requires less human involvement.
3)Memorizes terms:
Another benefit of machine language translation is its ability to memorize key terms and reuse them wherever they might fit.
Conclusion
In conclusion we can say that Machine translations enable people to have information in many languages, helping to understand it without knowing the language.
