When a translator works upon a translation, a complex series of operations belies the ostensibly simple process that is taking place. It has long been the goal of computational linguists to achieve fully automatic high quality machine translation, but to date that goal is some way off, nevertheless, with the means at our disposal today, is machine translation a viable choice?

Before proceeding, it would be well to analyse the translation process to gain some appreciation of the problems to be overcome by translation software...the process is thus:

The translator must arrive at a full understanding of the source text, the means of accomplishing this through a thorough understanding of the languages’:

• Grammar - The set of rules determining the use of a language.
• Syntax - The rules that determine the structure of sentences and, therefore, how grammatical such sentences are.
• Idioms - Terms or phrases whose meanings cannot be derived from their literal translation, but only by knowledge of their local usage e.g. ‘raining cats and dogs’.
• Semantics - The study of culturally specific meanings or concepts within a language.
• Cultural Framework - The basis for understanding the ‘patterns of activity’ of a discrete group of people.

Once this is accomplished, using an understanding of the same features of the target language, a conversion is made resulting in the finished translation.

So what technical methodology can machines employ to duplicate the process shown above?

The first and most basic is ‘dictionary-based’ translation. This is where translation is carried out in an analogous manner to a dictionary - word for word; however, this can often give a somewhat meaningless output with no connective theme. Nonetheless, it can form the ‘base’ upon which other methodology can be added.

The next method that can be used is that of statistical machine translation. This is a somewhat complex translation methodology, but in its simplest terms it takes two corpora of text and compares and matches each corpus against the other, thus arriving at a sort of equivalence and thus enabling it to produce a sort of statistically modelled output.

The last method is that of Interlingual machine translation. Interlingual machine translation takes a source language and transforms it into a language independent medium - a lingua (rather like the conversion of the alphabet into binary 1’s and 0’s). The target language will then be generated from this Interlingua. This is an important methodology because the concept of converting a language into an abstract representation before re-formation is a core principle used in artificial intelligence.

So has a combination of the aforementioned technological approaches led to machine translation being a viable choice? Well, at the moment no...or should I say not as a stand alone method. The trouble with language is that although it follows general rules, actual usage in real life is open to very wide variance, there are always linguistic exceptions and everyone uses widely different modes of expression, making it difficult for any mechanical method to account for this.

This being said, machine translation can often produce an acceptable output and can therefore be used as a timesaving device to quickly provide a loose translation of the mass of a corpus before it being passed to a translator for refining. Many translations agencies, do in fact, use such a procedure and if you were to pass a text to any translation service in the UK you would probably find that to be the case. This can ultimately only be for the good...as machine translation improves, the time required to carry out a translation will decrease and so, subsequently, will the cost.

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