2. The notion of sublanguage

Skilled human translators are able to adapt to various kinds of source text. Some translators can even start with poorly written source texts and produce translations that exceed the quality of the original. However, current machine translation systems strictly adhere to the principle of “garbage in — garbage out.” Therefore, if high quality translation is needed yet the source text is poorly written, forget about machine translation. There is more. Machine translation systems cannot currently produce high-quality translations of general-language texts even when well written. It is well-known within the field of machine translation that current systems can only produce high-quality translations when the source text is restricted to a narrow domain of knowledge and, furthermore, conforms to some sublanguage. A sublanguage is restricted not just in vocabulary and domain but also in syntax and metaphor. Only certain grammatical constructions are allowed and metaphors must be of the frozen variety (that is, used over and over in the same form) rather than dynamic (that is, creatively devised for a particular text).

Making the Decision

At first glance, post-editing may seem like a panacea [ˌpænə'siːə]. Why not use machine translation for everything and then have a human post-edit the raw output up to normal publication quality if needed? The answer is an economic one. For a source text not restricted to a sublanguage, the cost of post-editing can be very high. If the post-editor must consult both the source and target texts, the effort, and therefore the time and cost for post-editing can easily approach the cost of paying a professional translator to translate the source text from scratch without the benefit of the raw machine translation output. It is sometimes argued that raw machine translation, not matter how bad, is useful because it includes consistent use of equivalents for specialized terms. That argument does not stand up when modern translator tools are considered. Such tools include automatic lookup of the source terms in a termbase and display of the corresponding target-language terms.

Ambiguity

What makes machine translation so difficult? Part of the problem is that language is highly ambiguous when looked at as individual words. For example, consider the word “cut” without knowing what sentence the word came from. It could have been any of the following sentences:

He told me to cut off a piece of cheese.

The child cut out a bad spot from the apple.

My son cut out early from school again.

The old man cut in line without knowing it.

The cut became infected because it was not bandaged.

Cut it out! You’re driving me crazy.

His cut of the profit was too small to pay the rent.

Why can’t you cut me some slack?

I wish you could be serious, and not cut up all the time.

His receiver made the cut much sooner than the quarterback expected.

Hardly anyone made the cut for the basketball team.

If you give me a cut like that, I’ll have your barber’s license revoked.

Lousy driver! Look before you cut me off like that!

The cut of a diamond is a major determiner of its value.

If a computer (or a human) is only allowed to the word “cut” and the rest of the sentence is covered up, it is impossible to know which meaning of “cut” is intended. This may not matter if everything stays in English, but when the sentence is translated into another language, it is unlikely that the various meanings of “cut” will all be translated the same way. We call this property of languages “asymmetry”. We will illustrate an asymmetry between English and French with the word “bank.” The principal translation of the French word banque (a financial institution) is the English word “bank.” If banque and “bank” were symmetrical then “bank” would always be translated back into French as banque. However, this is not the case. “Bank” can also be translated into French as rive, when it refers to the edge of a river. Now you may object that this is unfair because the meaning of “bank” was allowed to shift. But a computer does not deal with meaning, it deals with sequences of letters, and both meanings, the financial institution one and the edge of a river one, consist of the same four letters, even though they are different words in French. Thus English and French are asymmetrical.

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