Machine Translation

A word has one or more well-defined meanings

Each meaning corresponds directly to an object (such as a horse) or an action (such as the action of jumping) or a quality (such as yellowness) in the real world.

The meaning of a sentence is built up by combining the meanings of individual words.

According to objectivism, real-world knowledge and the situation and flow of discourse can be safely ignored until the possible meanings of a sentence have been computed. Then the appropriate meaning is selected.

One problem with objectivism is that it assumes that the world divides itself up exactly one way into categories independently from how humans view the world, and that we then associate words with these pre-existing categories. However, there is not just one way to divide up the world. To take an extremely mundane example, there is not just one way for a butcher to divide up a beef. There are different cuts of meat in various countries. [Figure 8: 14k GIF] shows how beef is butchered in the United States and in Switzerland).

But this question of categorization goes much deeper than what you find in a grocery store. It pervades every aspect of our thinking, and it is dynamic. A few years ago, there was a television commercial in the United States extolling the size of the beef patty in the hamburgers from one chain of fast food restaurants as compared with the size of the patties used in the hamburgers sold by a competing chain of restaurants. In this commercial was the phrase:

Where's the beef?

This sentence took on a metaphorical meaning of challenging whether some project had produced sufficient visible results, even if it had nothing to do with beef or even with food. General language is full of such dynamic metaphor. We as humans are not usually even aware of minor shifts in meaning, because we are capable of handling them. Indeed, they give spice to language and are necessary to true creativity. However, dynamic metaphor is contrary to the assumptions of objectivism, since metaphorical meanings cannot be computed step-by-step.

All current approaches to processing language, including all commercial machine translation systems, are based on objectivism, whether the designers are aware of this fact or not. Indeed, objectivism has been so entrenched in the thinking of the Western world for hundreds of years that only recently are philosophers becoming aware of objectivism and considering alternatives. Unfortunately for machine translation, no one has yet conceived of a way to program a non-objectivist approach to language on a computer. This is why we can say that machine translation will not deal effectively with general language in the foreseeable future. Humans are able to deal with language without the constraints of objectivism. However, at this point, no one can foresee if or when computers will be able to deal with human language in a non-objectivist way.

Think of a microworld as a tree-house. Suppose two boys are sitting in a tree-house and suddenly are victims of severe amnesia. They could look around the tree-house for a long time and never be aware of an outside world even though there is a beam of light shining in from the outside [Figure 9: 119k GIF]. It is one thing to look at the beam of light, but it is an entirely different experience to look along the beam of light and through the hole so that you can see the outside world. The tree-house can be compared with a microworld. Currently, computers are based on assumptions that make it impossible to look along the beam of light and so they are restricted by the walls of some particular microworld.

Maybe some day, computers will be built that can look along the beam. In the meantime, computers are successful in dealing with human language according to how successfully language can be restricted to a microworld in which language does behave as if objectivism were an adequate portrayal of the nature of the world. The sublanguage used in a microworld must be carefully controlled.