LANGUAGE AS A BI-DIRECTIONAL TRANSFORMER

The main purpose of human communication is transferring some information—let us call it Meaning^[6]—from one person to the other. However, the direct transferring of thoughts is not possible.

FIGURE IV.2. Language functions like encoder / decoder in a communication channel.

Thus, people have to use some special physical representation of their thoughts, let us call it Text.^[7] Then, language is a tool to transform one of these representations to another, i.e. to transform Meanings to words when speaking, and the words to their Meaning when listening (see Figure IV.1).

It is important to realize that the communicating persons use the same language, which is their common knowledge, and each of them has a copy of it in the brain.

If we compare this situation with transferring the information over a communication channel, such as a computer network, the role of language is encoding the information at the transmitting end and then decoding it at the receiving end.^[8] Again, here we deal with two copies of the same encoder/decoder (see Figure IV.2).

Thus, we naturally came to the definition of natural language as a transformer of Meanings to Texts, and, in the opposite direction, from Texts to Meanings (see Figure IV.3).

FIGURE IV.3. Language as a Meaning Û Text transformer.

This transformer is supposed to reside in human brain. By transformation we mean some form of translation, so that both the Text and the corresponding Meaning contain the same information. What we specifically mean by these two concepts, Text and Meaning, will be discussed in detail later.

Being originally expressed in an explicit form by Igor Mel’čuk , this definition is shared nowadays by many other linguists. It permits to recognize how computer programs can simulate, or model, the capacity of the human brain to transform the information from one of these representations into another.

Essentially, this definition combines the second and the third definitions considered in the previous section. Clearly, the transformation of Text into Meaning and vice versa is obligatory for any human communication, since it implies transferring the Meaning from one person to another using the Text as its intermediate representation. The transformation of Meaning into Text is obligatory for the generation of utterances. To be more precise, in the whole process of communication of human thoughts the definition 1 given earlier actually refers to Meaning, the definition 2 to Text, and the definition 3 to both mentioned aspects of language.

FIGURE IV.4. Meaning Û Text many-to-many mapping.

With our present definition, language can be considered analogous to a technical device, which has input and output. Some information, namely Text, being entered to its input, is transformed into another form with equivalent contents.

The new form at the output is Meaning. More precisely, we consider a bi-directional transformer, i.e., two transformers working in parallel but in opposite directions. Text is the result of the activity of one of these transformers, and Meaning, of the other.

Programmers can compare such a device with a compiler, let us say, a C++ compiler, which takes a character file with the ASCII text of the program in the input and produces some binary code with machine instructions, as the output. The binary code corresponds to the meaning of the program. However, a compiler usually cannot translate the machine instructions back to a C++ program text.

As a mathematical analogy to this definition, we can imagine a bi-directional mapping between one huge set, the set of all possible Texts, and another huge set, the set of all possible Meanings (see Figure IV.4).

FIGURE IV.5. Metaphor of surface and deep structures.

The two sets, Texts and Meanings, are not quite symmetric in their properties. Only the Texts have an explicit expression, only they can be immediately observed or directly transferred from one person to another, while the Meanings reside in the brain of each person independently and cannot be immediately observed or assessed.

This is similar to the front panel of an electrical device: the lights and switches on its surface can be observed, while the electrical processes represented by the lights and controlled by the switches are deep^[9] under the cover of the device, and can only be guessed by watching the lights or experimenting with the switches.

Another metaphor of surface and deep structures of language is shown in Figure IV.5. We can directly observe the surface of water, but in order to learn what leaf is connected to what flower through common roots, we need to analyze what is under the surface. There is much more below the surface than on top of it, and only analysis of the deeper phenomena gives us understanding of the whole thing.

FIGURE IV.6. Two levels of representation.

All this is often considered as surface and deep levels of the representation of utterances (see Figure IV.6). The man on the picture cannot see the meaning of the text immediately and has to penetrate below the surface of the text to find its meaning.

Thus, the set of Texts is considered the surface edge of the Meaning Û Text transformer, while the set of Meanings gives its deep edge. The Meaning corresponding to the given Text at the depth is also called its semantic representation.

The transformation of Meaning into Text is called synthesis of the Text. The transformation to the inverse direction, that is from Text into Meaning, is called analysis of Text. Thus, according to our basic definition, natural language is both analyzer and synthesizer of Texts, at the same time.

This definition uses the notions of Text and Meaning, although they have been neither defined nor described so far. Such descriptions will be given in the following sections.

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