Philosophical Aspects of Neural, Probabilistic and Fuzzy Modeling of Language Use and Translation

Serious efforts to develop computerized systems for natural language understanding and machine translation have taken place for more than half a century. Some successful systems that translate texts in limited domains such as weather forecasts have been implemented. However, the more general the domain or complex the style of the text the more difficult it is to reach high quality translation. The same applies to natural language understanding. All systems need to deal with problems like ambiguity, lack of semantic coverage and pragmatic insight. In this article, some philosophical questions that underlie the difficulty of natural language understanding and good quality translation are first studied. These two areas of dealing with languages are actually closely related. Namely, for instance Quine's notion of indeterminacy of translation have shown that the problem of translation does not only hold for translation between different languages but similar problems are encountered when communication between users of same language is considered. The term intralingual translation has been used e.g. by Roman Jakobson. Intralingual translation relates to translation between languages and to the problem of sameness of meaning. In this article, arguments and methods of considering translation and meaning within the framework of continuous-valued multidimensional representations, probability theory, fuzzy sets and neural adaptive systems are considered.