[jweese]

Yes. For example, consider interlingual machine translation. Most systems today (like Google) use statistical MT that learns patterns from millions of examples. In interlingua, by contrast, you analyze the input sentence to form a language-independent representation of the sentence's meaning. Then you use that representation to generate a sentence in a new language.

As you might expect, this is basically impossible for wide-domain MT because we don't have unambiguous representations of the meaning of every sentence, and we don't necessarily know how to combine them, and there's a lot of non-compositional phrases, and on and on.

However, if we restrict ourselves to one small domain, interlingua can work. For example, the KANT system [1] is an interlingua that is built for translating technical manuals for Caterpillar products (bulldozers and so on). The input has to be written in a restricted subset of English (Caterpillar Technical English), but then you can analyze it exactly with hand-written rules, and produce exact output in the target language.

[1] http://www2.lti.cs.cmu.edu/Research/Kant/