Tag Archives: machine translation

Why it’s worth it to engage in rule-based translation

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).

Reflections on grammatical typologies

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.

Analyzing relative pronouns

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)

Powering MT with two-sided grammar: the case of ‘près de’

‘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).

Expanding on noun modulators

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.

New: Part-of-speech tagger for French language API

I have just published the POS-tagger for French language API, on RapidAPI. The use of the API is free for 1000 requests / month. No training necessary, it works immediately.

Further reflexions on the status of “I love you” in Corsican language

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.

More on polymorphic disambiguation…

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.

Autonomous MT system

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.

Word sense disambiguation: a hard case

Let us consider a hard case for word sense disambiguation, in the context of French to Corsican MT. But the same goes for French to English MT. It relates to French words such as: ‘accomplit’, ‘affaiblit’, ‘affranchit’, ‘alourdit’, ‘amortit’. The corresponding verbs ‘accomplir’ (to fulfill, to accomplish), ‘affaiblir’ (to weaken), ‘affranchir’ (to free), ‘alourdir’ (to burden), ‘amortir’ (to damp) have the same word for simple present and simple past at the third person singular: respectively ‘accomplit’, ‘affaiblit’, ‘affranchit’, ‘alourdit’, ‘amortit’. The upshot is that a single sentence such as: ‘Il affaiblit sa position.’ can be translated either into he weakens his position or into he weakened his position. If the context is unambiguous with regard to the sence of the discourse, the correct tense can be adequately chosen. But in the lack of informative context, it would be opportune to let the ambiguity prevail.

It should be pointed out that any such verbs are not rare. A more complete list includes: accomplit, affaiblit, affranchit, alourdit, amortit, anéantit, anoblit, aplatit, arrondit, assombrit, bannit, bâtit, blanchit, blondit, démolit, éblouit, emplit, enfouit, enhardit, enlaidit, ennoblit, envahit, épaissit, étourdit, exclut, franchit, glapit, investit, jaunit, jouit, munit, noircit, obéit, obscurcit, occit, périt, réagit, régit, réjouit, remplit, répartit, resplendit, rétrécit, rit, rougit, rouvrit, saisit, sévit, surgit.