3. History of MT
The idea of machine translation may be traced back to the 17th century. In 1629, Rene
Descarte proposed a universal language, with equivalent ideas in different tongues
sharing one symbol
.
The field of “machine translation” appeared in Warren Weaver’s Memorandum on
Translation (1949).
The French Textile Institute also used MT to translate abstracts from and into French,
English, German and Spanish (1970); Brigham Young University started a project to
translate Mormon texts by automated translation (1971).
More innovations during this time included MOSES, the open-source statistical MT
engine (2007), a text/SMS translation service for mobiles in Japan (2008), and a
mobile phone with built-in speech-to-speech translation functionality for English,
Japanese and Chinese (2009). Recently, Google announced that Google
Translate translates roughly enough text to fill 1 million books in one day (2012).
4. MT Approaches
There are two approaches of Machine
Translation.
Statical MT Technology
Rule Based MT Technology
5. Statical MT
Technology
Unpredictable translation quality
Poor out-of-domain quality
Does not know grammar
High CPU and disk space requirements
Inconsistency between versions
Good fluency
Good for catching exceptions to rules
Rapid and cost-effective development costs
provided the required corpus exists
6. Rule based MT technology
Consistent and predictable quality
Out-of-domain translation quality
Knows grammatical rules
High performance and robustness
Consistency between versions
Lack of fluency
Hard to handle exceptions to rules
High development and customization costs
11. Conceptual difference
• ‘wall’
• ‘corner’
• ‘leg’
• ‘leg’
• ‘blue’
German
Spanish
French
Spanish
Russian
• Fr. louer
• Sp. paloma
Wand ~ Mauer
esquina ~ rincón
jambe ~ patte ~ pied
pierna ~ pata ~ pie
голубой ~ синый
hire ~ rent
pigeon ~ dove
12. Applications
While no system provides the holy grail of fully automatic high-quality
machine translation of unrestricted text, many fully automated systems
produce reasonable output.
The International Society of Automation contributed 3.072 million euros for
the creation of MT.
With the recent focus on terrorism, the military sources in the United States
have been investing significant amounts of money in natural language
engineering.
Currently the military community is interested in translation and processing
of languages like Arabic, Pashto and Dari.
The notable rise of social networking on the web in recent years has created
yet another niche for the application of machine translation software – in
utilities such as Facebook, or instant messaging clients such as Skype,
GoogleTalk, MSN Messenger, etc. – allowing users speaking different
languages to communicate with each other.
13.
14.
15. Advantage of MT
When time is a crucial factor, machine translation can save the
day. You don't have to spend hours poring over dictionaries to
translate the words. Instead, the software can translate the
content quickly and provide a quality output to the user in no
time at all.
The next benefit of machine translation is that it is comparatively
cheap. Initially, it might look like a unnecessary investment but in
the long run it is a very small cost considering the return it
provides. This is because if you use the expertise of a professional
translator, he will charge you on a per page basis which is going
to be extremely costly while this will be cheap.
Confidentiality is another matter which makes machine
translation favorable. Giving sensitive data to a translator might
be risky while with machine translation your information is
protected.
A machine translator usually translates text which is in any
language so there is no such major concern while a professional
translator specializes in one particular field.
16. Disadvantage of MT
Accuracy is not offered by the machine translation on a consistent
basis. You can get the gist of the draft or documents but machine
translation only does word to word translation without
comprehending the information which might have to be corrected
manually later on.
Systematic and formal rules are followed by machine translation so
it cannot concentrate on a context and solve ambiguity and neither
makes use of experience or mental outlook like a human translator
can.