Is That a Fish in Your Ear?

It was theoretically possible at the start of the machine-translation adventure (and it soon became practically possible as well) to store a set of words on a computer, divided into the grammatical classes the Greeks and Romans devised. It was equally possible to store two sets of words, one for Russian, one for English, and to tell the computer which English word matched which Russian one. More dubious was the proposition implicit in Weaver’s fable that you could bring people down from their separate towers to the common basement—that’s to say, tell a computer what to do to unwrap the meaning of a sentence from the form of the sentence itself. To do that, the computer would first need to know the entire grammar of a language. It would have to be told what that consists of. But who knows the entire grammar of English? Every language learner quickly realizes that systematic regularities are frequently overruled by exceptions of arbitrary kinds. Every speaker of a native language knows that she can (and frequently does) break the “rules” of grammar. A complete linguistic description of any language remains an aspiration, not a reality. That is one of the two reasons why the first great phase of machine translation hit the skids. The second is that even humans, who can be assumed to be in full possession of the grammar of their language, still need a heap of knowledge about the world in order to fix the meaning of any expression—and nobody has figured out how to get a computer to know what a sentence is about. A classic conundrum that computers could not solve is to attribute the correct meanings to the words in the following two sentences: “The pen is in the box” and “The box is in the pen.” Understanding them calls on knowledge of the relative sizes of things in the real world (of a pen-size box and a sheep pen, respectively) that can’t be resolved by dictionary meanings and syntactic rules. In 1960, the eminent logician Yehoshua Bar-Hillel, who had been hired by MIT specifically to develop “fully automated high-quality translation,” or FAHQT, sounded a testy retreat:

I have repeatedly tried to point out the illusory character of the FAHQT ideal even in respect to mechanical determination of the syntactical structure of a given source-language sentence … There exist extremely simple sentences in English—and the same holds, I am sure, for any other natural language—which, within certain linguistic contexts, would be … unambiguously translated into any other language by anyone with a sufficient knowledge of the two languages involved, though I know of no program that would enable a machine to come up with this unique rendering unless by a completely arbitrary and ad hoc procedure …[148]

That pretty much put an end to easy money from the grant-giving foundations. But the establishment of the European Union in 1957 provided a new political impetus—and a new funding source—for the development of the tools that Bar-Hillel thought impossible. Ambitions were scaled down from FAHQT to more feasible tasks. As computers grew in power and shrank in size, they could more easily be relied upon for tasks that humans find tiresome, such as checking that a given term has been translated the same way each time it occurs in a long document. They could be used for compiling and storing dictionaries not just of technical terms but of whole phrases and expressions. The era not of fully automatic translation but of CAT—computer-aided translation—began. Private companies started developing proprietary systems, for although the big demand came from transnational entities such as the EU, there was a real need for such tools among major companies producing aircraft, automobiles, and other goods to be sold all over the world.