introduces the notion linguistically possible translation which provides a useful methodological concept for work in Machine Translation (see also ). A system which implements the notion `linguistically possible translation' employs linguistic knowledge only. Clearly, in order to produce the correct or best translation such a system has to be augmented with other (artificial intelligence) components. However, at the current state of technology it seems unrealistic to expect that these other components can be constructed in the near future. Therefore, a more realistic goal for MT consists of the construction of systems implementing the notion `linguistically possible translation'. Such systems may also be of practical interest, because it may be possible to augment such restricted systems with a component that interacts with the user -- for example in the case of difficult disambiguation problems. Thus the user can provide help in order to extract the best translation from the computed set of linguistically possible translations. It is assumed that the overall best translation is in fact to be found (in a significant number of cases) among this set. This chapter therefore will be focused on the notion `linguistically possible translation', rather than on translation in general.
Consider a class of MT systems which employ linguistic knowledge only. In such a system, a source text is assigned a set of meaning representations according to the rules of the grammar of the source language. On the basis of such a meaning representation the target grammar then produces a set of target sentences for this grammar. Such a system thus produces a set of linguistically possible translations.  assumes that the relation `linguistically possible translation' (lpt) is a symmetric relation. Thus, ttarget lpt tsource iff tsource lpt ttarget. On the other hand, the relation `best of linguistically possible translations' is not. For example assume that disambiguation based on knowledge of the world is outside the linguistic components. The asymmetry occurs if the translation of some (unambiguous) source sentence is ambiguous, and where furthermore the `added' reading of the target sentence is to be preferred for extra-linguistic reasons. An example can be constructed using the famous Bar-Hillel sentence, cf. figure 5.1.
This view of translation is extremely poor. For example, it does not take world knowledge into account as we saw above. Moreover, there are many other factors that could be taken into account in defining linguistically possible translations, e.g. preservation of style, (indirect) speech act, honorifics, etc. It is hoped (and expected) that an approach based on the poor view described can be useful as a basis for future richer views.
An important question of translation is whether there always is a meaning-preserving translation. It may be the case that there are meanings in one language that are not expressible at all in some other language (for some discussion cf. [42,47]). It may even be the case that one cannot know whether the meaning expressed in two languages is the same (cf. ). These are important questions, but the approach outlined here does not depend on how they are answered. Our approach is concerned only with the case where the same meaning can be expressed in both languages. Our question is `how to describe possible translations', not `is translation possible'.
The symmetry of the linguistically possible translation relation provides the motivation for reversible MT systems [51,44]. If the lpt relation from language l1 to l2 is in fact the same relation as going from l2 to l1, then it seems very natural to try to characterize this relation only once -- and to construct a program which is able to compute this relation, given this single characterization, in both directions.
Such an approach has a number of advantages, most of which coincide with the advantages already mentioned in chapter 1. Thus, a reversible architecture has