Currently transferring the whole thing to python. The main goal is to realize a module for versatile grammatical disambiguation, i.e. a disambiguation module per grammatical type, suitable for many languages, without major and complicated code changes. The flexibility allowed by the dictionaries and the different types of lists in python seems to be better suited to this project.
Disambiguation is an essential process in machine translation. Sometimes, however, it seems more rational and logical to leave an ambiguity in the translation. This is the case when (i) there is an ambiguous word in the sentence to be translated; and (ii) the context does not provide an objective reason to choose one of the two occurrences. It seems that in this case, the best translation is the one that leaves the ambiguity intact.
Let’s take an example. Consider the following French sentence: ‘Son palais était en feu.’. The French word ‘palais’ is ambiguous, because it corresponds in English and in Corsican to two different words (palace, palazzu and palate, palatu).
Thus, we have 3 possibilities of translation:
The third translation, in my opinion, is better, because it points out that the context is insufficient to choose one of the two alternatives.
Consider now, on the one hand, the following sentence: ‘Il avait mangé du piment fort. Son palais était en feu.’ Now the context provides an objective motivation to choose one of the two occurence. This yields the following translation: He had eaten some hot pepper. His palate was on fire.
On the other hand, consider the following sentence: ‘Les ennemis du prince avaient lancé des engins incendiaires. Son palais était en feu.’ We also have here an objective reason to choose the other alternative. It translates then: The prince’s enemies had thrown incendiary devices. His palace was on fire.
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.
Italian has ‘prepositions followed by articles’ (preposizione articolate). This is a specific grammatical type, which refers to a word (e.g. della) that replaces a preposition (di) followed by an article (la):
il lo l’ la i gli le di del dello dell’ della dei degli delle a al allo all’ alla ai agli alle da dal dallo dall’ dalla dai dagli dalle in nel nello nell’ nella nei negli nelle su sul sullo sull’ sulla sui sugli sulle
This specific grammatical type also corresponds to:
This raises the general problem of the number of grammatical types we should retain. Should we create new grammatical types beyond the classical ones, in order to optimise translators and NLP in general? What is the best grammatical type to retain for ‘prepositions followed by an article’: a new primitive one or a compound one (always keeping Occam’s razor in mind)? A preposition followed by an article behaves like a preposition for words on its left, and like an article for words on its right.
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:
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.
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:
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:
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.
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.
The question of choosing the best system to solve the problems posed by word disambiguation in the field of translation seems to be linked to the AGI control problem (how to avoid that an AGI finally turns out to be harmful for its creators). It seems that when we have the choice between several methods to develop an AI, it is wiser to choose the one that allows a better control of the AGI. As far as machine translation is concerned, we should thus prefer in this regard the method that emulates human reasoning, and that produces a response that can be broken down step by step into the reasoning that leads to it. This makes it possible to accurately determine the cause of an error, but also to remedy it. This problem does not only concern machine translation, but has a somewhat extended scope. For grammatical disambiguation concerns machine translation, but also the understanding of natural language, and disambiguation according to context, in the very absence of any translation.
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.
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: