Tag Archives: rule-based machine translation

Dictionary = Corpus?

As far as machine translation is concerned, it seems that the best thing is to combine the best of the two approaches: rule-based or statistic-based. If it were possible to converge the two approaches, it seems that the benefit could be great. Let us try to define what could allow such a convergence, based on the two-sided grammatical approach. Let us try to illustrate this with a few examples.
To begin with, u soli sittimbrinu = ‘le soleil de septembre’ (the sun of September). In Corsican language, sittimbrinu is a masculine singular adjective that means ‘de septembre’ (of September). In French, ‘de septembre’ is–from an analytic perspective–a preposition followed by a common masculine singular noun. But according to the two-sided analysis ‘de septembre’ (of September) is also–from a synthetic perspective–a masculine singular adjective. This double nature, according to this two-sided analysis of ‘de septembre’, allows in fact the alignment of ‘de septembre’ (of September) with sittimbrinu.
More generally, if we define words or groups of words according to the two-sided grammatical analysis in the dictionary, we also have an alignment tool, which can be used for a translation system based on statistics, in the same way as a corpus. Thus, if it is sufficiently provided, the dictionary is also a corpus, and even more, an aligned corpus.

Grammatical word-disambiguation again and again

The main difficulty here seems to lie in the adaptation of the grammatical disambiguation module. Indeed, for the French language, such a module performs disambiguation with respect to about 100 categories. The number of pairs (or 3-tuples, 4-tuples, etc.) of disambiguation, for French, is about 250. The question is: when we change languages, how many categories of n-tuples of disambiguation does this result in? In particular, when one switches from French to Italian, does this result in a big change in the categories to be disambiguated?

Let’s take an example, with a particular category of words to disambiguate. One such category is for example AQfs/Vsing3present (feminine singular adjective or verb in the 3rd person singular present tense). A word in Italian that belongs to this type is ‘stanca’. So we have both uses:

  • ‘è stanca’ (she is tired): AQfs
  • stanca il cavallo’ (it tires the horse): Vsing3present
    In French, we don’t have this kind of disambiguation category directly because the category concerned is broader than that: it includes at least the 1st person singular of the present. Thus we have the word ‘sèche’, which belongs to this type of disambiguation category:
  • ‘la feuille est sèche’ (the leaf is dry): AQfs
  • ‘je sèche mes cheveux’ (I dry my hair): Vsing1present
  • ‘il sèche sa chemise’ (he dries his shirt): Vsing3present

Of course, the code that allows the disambiguation of AQfs/Vsing1present/Vsing3present should also allow the derivation of the disambiguation of AQfs/Vsing3present. But this gives an idea of the kind of problems that arise and the adaptation needed.

If the types of disambiguation are very different from one language to another, it will be necessary to have a disambiguation module which is capable of adapting to many new types of disambiguation and which is therefore very flexible. This appears to be a considerable difficulty for the creation of an eco-system. It seems that Apertium, faced with this difficulty, has chosen a statistical module as a solution for its eco-system. However, the question of whether such a flexible module, adaptable without difficulty from one language to another, is feasible in the context of rule-based MT, remains an open question.

Adjective modifiers again

We will consider again a category of words such as ‘very’, when they precede an adjective. Traditionally, this category is termed ‘adverbs’ or ‘adverbs of degree’, but we prefer ‘adjective modifier’, because (i) analytically, they change the meaning of an adjective and (ii) synthetically, an adjective modifier followed by an adjective is still an adjective. A more complete list is: almost, absolutely, badly, barely, completely, decidedly, deeply, enormously, entirely, extremely, fairly, fully, greatly, hardly, highly, how, incredibly, intensely, less, most, much, nearly, perfectly, positively, practically, pretty, purely, quite, rather, really, scarcely, simply, somewhat, strongly, terribly, thoroughly, totally, utterly, very, virtually, well.

If we look at sentences such as: il est bien content (he is very happy, hè beddu cuntenti), ils étaient bien contents (they were very happy, erani beddi cuntenti), elle serait bien contente (she would be very happy, saria bedda cuntenti), elles sont bien contentes (they are very happy, sò beddi cuntenti), we can see that the modifier of the adjective ‘bien’ is rendered as very in English and in Corsican as:

  • bellu/beddu: singular masculine
  • belli/beddi: plural masculine
  • bella/bedda: feminine singular
  • belle/beddi: feminine plural

This shows that the adjective modifier is invariable in French and English, but varies in gender and number in Corsican. Thus, in Corsican grammar, it seems appropriate to distinguish between:

  • singular masculine adjective modifier
  • plural masculine adjective modifier
  • singular feminine adjective modifier
  • plural feminine adjective modifier

On the other hand, such a distinction does not seem useful in English and French, where the category of ‘adjective modifier’ is sufficient and there is no need for further detail.

Grammatical word-disambiguation again

The challenge is especially that of generalizing the grammatical word-disambiguation to several languages. Creating a module of grammatical word-disambiguation for each language appears to be a long and arduous task. This seems to be the main difficulty. But if a module specific to a given language can be generalized to several other languages, this could be an important advance in the field of rule-based machine translation (which simulates human reasoning seems to me a more appropriate term).

We can describe the problem more precisely. We have about 100 grammatical categories for a given language. We also have about 300 ambiguous grammatical types – to fix ideas – which are: e.g., adverb or preposition, singular masculine noun or singular masculine adjective, etc. The problem is to describe an algorithm to remove the ambiguity and determine the corresponding grammatical type according to the context.

Now rewriting the complete module of disambiguation by grammatical type, so that it can be used and adapted to other languages (Italian in the first place). It remains to be seen if this can be done.

On the implementation of grammatical disambiguation

Grammatical disambiguation – i.e. whether ‘maintenant’ is and adverb (now) or the gerundive (maintaining) of the verb ‘maintenir’ – seems to be the crucial issue for the adoption of the rule-based model or statistical model for machine translation. This problem is widespread and seems to concern all languages. For the French language, this problem of grammatical disambiguation concerns about 1 word out of 7. Effective grammatical disambiguation is difficult to implement. The advantage of adopting the statistical method for grammatical disambiguation is that the same method can be generalized and used for several languages. In the case of the rule-based model, the module of grammatical disambiguation must be rewritten for each language, which generates considerable complexity and requires a very significant development time. Therefore, a rule-based method for grammatical disambiguation that can be easily applied to several languages would be of great interest. This seems to be the main difficulty that rule-based machine translation is designed to overcome.

But if we want an artificial intelligence that not only provides an (mostly accurate) answer without being able to really explain its reasoning, but is truly able to emulate human reasoning and to justify and describe step by step the reasoning that leads to its answer, then it is worth the effort.

The status of adjective modifiers

What is the status of adjective modifiers (tant, tout juste, un rien, un tantinet, très, extrêmement, … = so much, just a little, a little, a little, very, extremely, …) in the present grammatical typology? Adjectives are defined as noun modifiers. So adjective modifiers would be modifiers of noun modifiers? This sounds intriguing. In reality, we do not have the concept of ‘modifiers of modifiers’. In fact, we have the following rules:

  • a verb modifier followed by a verb is a verb
  • a determinant modifier followed by a determinant is a determinant
  • and generally speaking, a modifier of an X followed by an X is an X (where X is a given grammatical type)
    So a noun modifier followed by a noun is a noun, i.e. an adjective followed by a noun is a noun. For example: ‘un très beau livre’ (a very nice book), where ‘very’ is an adjective modifier, ‘nice’ is an adjective, i.e. a noun modifier, and ‘book’ is a noun.
    Hence finally, ‘an adjective modifier is a modifier of a noun modifier’ reads as follows: an adjective modifier is a modifier of [noun modifier].

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)

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.