Here is a problem for a human intelligence (or an AGI): we have a dictionary (with words, lemmas and grammatical types) in a language A and a second dictionary in a language B. If we have an extensive corpus of each of the two languages, is it possible to create a translation dictionary from A to B, and how? To take an example: if the two languages were French and English, we would have to associate ‘cheval’ with ‘horse’, etc. in the final translation dictionary, and so on for all the words of language A.
Rule-based translation is difficult to implement. The main difficulty encountered is taking into account the groups of words, so as to be on a par with statistics-based translation. The main problems in this regard are (i) polymorphic disambiguation; and (ii) building a fair typology of grammatical types. But once these steps begin to be mastered, there are many advantages. What seems essential here is that with the same piece of software, both machine translation and text analysis can be carried out. Among the modules that are easy to implement are the following:
lemmatizer
part-of-speech tagger
singularizer
pluralizer
grammar checker
type extractor: a module that allows you to extract words from a text according to their grammatical category
For the implementation of rule-based translation provides the machine with some inherent understanding of the text, in the same way that a human being does. To put it in a nutshell, it is better artificial intelligence.
Finally, other modules, more advanced, seem possible (to be confirmed).
It is useful to point out the differences that may exist between different grammatical typologies. The classical grammatical taxonomy is essentially aimed at teaching and comprehension. It therefore has a pedagogical purpose. On the other hand, the taxonomy that is useful for rule-based machine translation has a different purpose: it aims essentially at allowing disambiguation, both grammatically and semantically, because ambiguity is a fundamental and very common problem in this particular context. Such a typology essentially focuses on the location of word types, on the structures encountered in the sentence. This explains why typologies can be different, as they have different goals and purposes.
What is the status of ‘relative pronouns’ of classical grammar within the present conceptual framework? Traditionally, a distinction is made between simple relative pronouns (qui, que, dont, où ; who, what, whose, where) and compound relative pronouns (à qui, pour lesquelles, à côté duquel, etc.; to whom, for whom, beside whom, etc.). If we look first at simple relative pronouns, the category does not seem satisfactory, in particular because of the presence of ‘qui’ (who) and ‘que’ (what), whose grammatical role appears, in the present context, to be quite different. Consider the two short sentences: ‘la maison que j’habite est grande’; et ‘l’homme qui parle est grand’. (the house I live in is big and the man who speaks is tall.). As these two examples illustrate, the structures following ‘que’ and ‘qui’ appear different. Here, ‘que’ is followed by a personal pronoun (‘j’habite’: I live) and a conjugated verb; and ‘qui’ is followed directly by a conjugated verb (‘parle’: speaks). From our present perspective, these are inherently different structures. Here, it turns out that ‘dont’ and ‘où’ admit the same type of structure as ‘que’. Thus, the homogeneous category, from our point of view, is formed here by ‘que’, ‘dont’, ‘où’, but not by ‘qui’. If we extend this analysis to other words, by searching for those who could fit into this category, we also find: ‘duquel’ (= de lequel; from which), ‘de laquelle’, ‘desquels’ (= de lesquels; from which), ‘desquelles’ (= de lesquelles; from which), ‘auquel’ (à lequel), à laquelle, ‘auxquels’ (à lesquels), ‘auxquelles’ (à lesquelles). But we also have all forms of the same type built from another preposition than ‘de’ or ‘à’: ‘sur lequel’, ‘sur laquelle’, …, ‘par lequel’, ‘par laquelle’, ‘avec lequel’, etc. Les pronoms relatifs composés classiques tels que ‘à qui’, ‘pour lesquelles’, ‘à côté duquel’, etc.; to whom, for whom, beside whom, etc.), s’intègrent également naturellement dans cette catégorie. But from the point of view of two-sided grammar, ‘à l’aide duquel’, ‘au moyen de laquelle’, ‘à la suite de quoi’, ‘à l’aide de qui’, etc. (with the help of which, by means of which, as a result of which, with the help of whom, etc.) also belong to this category. (to be continued)
‘près de’ (near) is considered to be a prepositive locution. From the viewpoint of two-sided grammar, it is (synthetically) a preposition, made up (analytically) of an adverb (‘près’) followed by the preposition ‘de’. In Corsican language, this is translated as vicinu à. But this grammatical analysis does not solve all cases, as the example above shows. Because in the sentence ‘depuis près de dix ans, il travaillait’ (for almost ten years, he has been working), ‘près de’ (almost; guasgi) has a different grammatical role. According to classical analysis, it would rather be an adverb. In the present conceptual framework, we will analyze ‘près de’ (almost; guasgi) in ‘depuis près de dix ans, il travaillait’ (for almost ten years, he has been working) as a modulator of the cardinal determinant ‘dix’ (ten), i.e. as a modulator of cardinal determinant. A prototype implemented with this type of grammatical analysis then gives the correct translation, where ‘near’ is replaced by guasgi (nearly) . It seems that two-sided grammar is beginning to produce interesting results (to be confirmed).
Let’s take a closer look at noun modulators, especially common noun modulators. We have seen that adjectives could be considered, in the present conceptual framework, as noun modulators. In this context, the question arises, are there other forms of noun modulators? It seems that there are.
Let us consider elements of sentences such as ‘bois de châtaignier’ (chestnut wood; legnu castagninu) or ‘oiseau de proie’ (bird of prey; aceddu di preda). In ‘bois de châtaignier’, ‘de châtaignier’ seems to play the role of noun modulator, in the same way as an adjective. In traditional grammar, ‘de châtaignier’ is considered as a noun complement. In the present framework, it would be a noun modulator, since it clarifies and restricts the meaning of the noun ‘bois’ (wood; legnu). The role of ‘de proie’ in ‘oiseau de proie’ is identical, as it acts as a modulator of the name ‘bird’.
Interestingly, it turns out that the comparison between languages tends to validate this type of analysis. Indeed, ‘bois de châtaignier’ is better translated in Corsican language by legnu castagninu than litterally by legnu di castagnu (chestnut wood); and in this case, castagninu (of chestnut) is an adjective, i.e. a noun modulator. Thus, castagninu and di castagnu being equivalent here, confirming in both cases their same nature of adjective modulator.
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Let us briefly recall the problem: translating ‘I love you’ might sound trivial, but it’s not. In fact, ‘ti amu‘ is not the best translation. The best translation is ‘ti tengu caru‘ when addressed to a male person, or ‘ti tengu cara‘ when addressed to a female person. Hence the proposed preliminary translation ‘ti tengu caru/cara‘. Such rough translation requires further disambiguation, but on what precise grounds?
Let us look at the issue from an analytical perspective. It appears that we need to assign a reference to the pronoun ‘te’ (you, ti). The latter could be identified according to the context, depending on whether the person ‘te’ refers to is male or female. At this stage, it appears that it is better to consider that the personal object pronoun has an inherent gender: masculine or feminine. This gender does not affect the pronoun itself which remains ‘te’ (you, ti) independently of the gender, but it does have an effect on the words that depend on it, i.e. the adjective caru/cara in Corsican, in the locution ti tengu caru/cara. The upshot is: in this case, ‘te’ (you, ti) is a personal object pronoun, masculine or feminine, whose inherent ambiguity can be solved according to the context.
Let’s take another look at polymorphic disambiguation. We shall consider the French word sequence ‘nombre de’. The translation into Corsican (the same goes for English and other languages) cannot be identical, because ‘number of’ can be translated in two different ways. In the sequence ‘mais nombre de poissons sont longs’ (but many fish are long), ‘number of’ is an indefinite determiner: it translates as bon parechji (many). On the other hand, in the sequence ‘mais le nombre de poissons est supérieur à dix’ (but the number of fish is greater than ten), ‘nombre de’ is a common name followed by the preposition ‘de’: it is translated by numaru di (number of). Statistical MT does usually better than human-like (rule-based) MT at polymorphic disambiguation (I did a test with both sentences with Deepl and Google translate, and both of them successfully solve the relevant polymorphic disambiguation), but it turns out that human-like (rule-based) MT is also capable of handling that.
Let us speculate about what could be an autonomous MT system. In the present state of MT we provide rules and dictionary to the software (rules-based translation) or we feed it with a corpus regarding a given pair of languages (statistical MT). But let us imagine that we could do otherwises and build an autonomous MT system. We provide the MT system with a corpus regarding a given source language. It analyses, first, the thoroughly this language. It begins with identifying single words. It creates then grammatical types and assigns then to the vocabulary. It also identifes locutions (adverbial, verbal, adjective locutions, verb locutions, etc.) and assigns them a grammatical type. The MT system also identifies prefixes and suffixes. It also computes elision rules, euphony rules, etc. for that source language. Now the autonomous MT system should, second, do the same for the target language. The MT system creates, third, a set of rules for translating the source language into the target one. For that purpose, the MT system could for example assign a structured reference to all these words and locutions. For instance, ‘oak’ in English refers to ‘quercus ilex’, ‘cat’ refers’ to ‘felis sylvestris’. For abstract entities, we presume it would not be a trivial task… Alternatively but not exclusively, it could use suffixes and exhibit morphing rules from the source language to the target one.
Is it feasible or pure speculation? It could be testable. Prima facie, this sounds like a different approach to IA than the classical one. It operates at a meta-level, since the MT system creates the rules and in some respect, builds the software.
