The issue of pair reversal: it goes as follows: Suppose your have a given translation pair A>B that translates language A into language B, how hard is it to build the reverse pair B>A? Now the current instance of this problem goes as follows: given the French>Italian pair, how hard is it to build an Italian>French pair? To state it more explicitly : could AI help build a reverse pair in a very short time. Arguably, if AI could build such reverse pair shortly, it seems it would be some kind of breakthrough. Supposedly, we do not expect a 100% efficiency and accuracy in this reversal process, but if some 98% or 99% were possible, it would do the job. For AI within MT is not only targeted at translating, it is also targeted at constructing translation engines.
Just tested pair reversal from French-Italian to Italian-French. Well, some 70% can be made automatically, but a big issue is still remaining, that relates to the disambiguation of Italian words. The disambiguation engine seems to be the crux of the matter here. The uupshot is that the entire disambiguation module needs to be rewritten, in order (if possible) to be language-related. The new module must be more AI-focused. If successful, it could open the path to the (somewhat) fast construction of a multi-language ecosystem with a rule-based MT architecture.
Let us consider the following French sentence: Le comté de Kronoberg est un comté suédois dont le nom signifie en français ‘Couronne de montagne’. It translates into Corsican: A cuntea di Kronoberg hè una cuntea svedese chì u so nome significheghja in francese ‘Curona di muntagna’. (The County of Kronoberg is a Swedish county whose name means in French ‘Mountain crown’.) But it should be translated more accurately as: A cuntea di Kronoberg hè una cuntea svedese situata in u sudu di u paese, è chì u so nome significheghja in corsu ‘Curona di muntagna’ since the words significheghja in francese (means in French) are utterly false.
Now a semantic difficulty is lurking whose core can be related to self-reference: How should we translate ‘Cette phrase est en français’ ? Self-reference stems here from ‘cette phrase’ (this sentence). Litterally, it translates into: This sentence is in French). But a sense-preserving translation would be: This sentence is in English).
A much complicated instance of self-reference within translation is as follows: ‘Cette phrase ne comprend que sept mots’ (This sentence contains only seven words). It translates into Corsican: ‘Ss’infrasata ùn cumprendi ch’è setti paroli. It is also true of the Corsican translation, but false of the English one, which includes only six words. Arguably, a better English translation, which is sense-preserving is then: This sentence contains only six words. Such translation ability is currently beyond the scope of present MT. We can tag it as an ability that would be required from superintelligent MT. It would then include: identifying sef-referent parts of discourse, such as: this sentence, these words, this proposition, this paragraph, this text, … But not all self-referring discourse is concerned here. For example, the Liar paradox (this sentence is false) is irrelevant here, since we only place ourselves from the standpoint of MT. Interestingly, such superintelligent ability also requires some meta-knowledge, i.e. the language of the source text and of the target text. For a shift from the source language to the target language is needed here.
We will be interested in a series of posts to try to define what is required of an AGI (Artificial General Intelligence) in order to reach the level of superintelligence in MT (machine translation). (All this is highly speculative, but we shall give it a try.) One of the difficulties that arise in machine translation relates to the translation of expressions. This leads us to mention one of the required skills of a superintelligence. It is the ability to identify an expression within a text in a given language and then to translate it into another language. Let us mention that expressions are of different types: verbal, nominal, adjectival, adverbial, … To fix the ideas we can focus here on verbal expressions. For example, the French expression ‘couper les cheveux en quatre’ (litterally, cut the hairs in four, i.e. to split hairs), which translates into Corsican language into either castrà i falchetti (litterally, to chastise the hawks) or castrà i cucchi (litterally, to chastise the cuckoos). In order to properly translate such an expression, a superintelligence must be able to:
identify ‘couper les cheveux en quatre’ as a verbal expression in a French corpus
identify castrà i falchetti as a verbal expression within a Corsican corpus
associate the two expressions as the proper translation of each other
It appears here that such an aptitude falls under the scope of AGI (Artificial general intelligence).
Here is a short follow-up to the ‘issue of pair reversal’ regarding language pairs. It seems some 90% could be achieved in this reversal process. What is lacking here is an adequate handling of disambiguation. Let us focus on one example. For it is patent in the above example, where Italian ‘venti’ is ambiguous between masculine plural noun (venti, wings) a numeral (vinti, twenty). But such specific ambiguity relating to grammatical types does not exit in French. The upshot is that disambiguation between grammatical types is specific to one given source language, at least in part. It this difficulty could be overcome, a rough 95% of the automatic process would finally be achieved.
(Obviously the current translation is not of an acceptable quality for publication: some 90% at least is in order…)
Anyway, handling sucessfully disambiguation in many languages appears to be the crux matter here. If AI could build sucessfully such disambiguation modules, it seems rule-based translation as a fast-growing ecosystem would be feasible.
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Let us consider superintelligence with regard to machine translation. To fix ideas, we can propose a rough definition: it consists of a machine with the ability to translate with 99% (or above) accuracy from one of the 8000 languages to another. It seems relevant here to mention the present 8000 human languages, including some 4000 or 5000 languages which are at risk of extinction before the end of the XXIth century. It could also include relevantly some extinct languages which are somewhat well-described and meet the conditions for building rule-based translation. But arguably, this definition needs some additional criteria. What appears to be the most important is the ability to self-improve its performance. In practise, this could be done by reading or hearing texts. The superintelligent translation machine should be able to acquire new vocabulary from its readings or hearings: not only words and vocabulary, but also locutions (noun locutions, adjective locutions, adverbial locutions, verbal locutions, etc.). It should also be able to acquire new sentence structures from its readings and enrich its database of grammatical sentence structures. It should also be able to make grow its database of word meanings for ambiguous words and instantly build the associate disambiguation rules. In addition, it should be capable of detecting and implementing specific grammatical structures. It seems superintelligence will be reached when the superintelligent translation machine will be able to perform all that without any human help.
Let us speculate now on how this path to superintelligent translation will be achieved. We can mention here:
a quantitative scenario: (i) acquire, fist, an ability to translate very accurately, say, 100 languages. (ii) develop, second, the ability to self-improve (iii) extend, third, the translation ability to whole set of 8000 human languages.
alternatively, there could be a qualitative scenario: (i) acquire, first, an ability to translate somewhat accurately the 8000 languages (the accuracy could vary from language to language, especially with rare endangered languages). (ii) suggest improvements to vocabulary, locutions, sentence structures, disambiguation rules, etc. that are verified and validated by human (iii) acquire, third, the ability to self-improve by reading texts or hearing conversations.
it is worth mentioning a third alternative that would consist of an hybrid scenario, i.e. a mix of quantitative and qualitative improvements. It will be our preferred scenario.
But we should provide more details on how these steps could be achieved. To fix ideas, let us focus on the word self-improvement module: it allows the superintelligent machine translation to extend its vocabulary in any language. This could be accomplished by reading or hearing new texts in any language. When facing a new word, the superintelligent machine translation (SMT, for short) should be able to translate it instantly into the 8000 other languages and add it to its vocabulary database.
To give another example, another module would be locution self-improvement module: it allows the superintelligent machine translation to extend its locution knowledge in any language.
Also relevant to this topic is the following question: could SMT be achieved without AGI ( general AI)? We shall address this question later.
Performing now a new open test, with the first 100 (more or less) words of the ‘article of the day’ from French wikipedia: we get 94,02% = 1 – (8 / 134). Several errors (5) result from lack of vocabulary. There are also some grammatical errors (da instead of par o pà, in diducendu instead of diducendu ni) and lastly, the diambiguation of polaccu (Polish) which is erroneous. The disambiguation of ‘partie’ is correct since it can be translated into parti (part)or into partita (gone, party).
Iterated open tests that that there is an average 50% of errors that result from lack of vocabulary. This type of error should be easy to tackle, inasmuch as it does not concerns rare words. Reasonably, a target of 96% or 97% should be attainable on this basis.
The test is ‘open’ in the sense that it can be verified here.
It is kind of a minor breakthrough. The translation of French ‘en même temps que’ (at the same time as) is somewhat hard, in that it can take two different forms: either à tempu à or à tempu ch’è, depending on the context. The above examples tackle this sort of difficulty (although not exhaustively).
There exists priority translation pairs, from the standpoint of endangered languages. Such notion of a priority pair (the most useful pair for the current users of the endangered language), regarding a given endangered language. For example, French to Corsican is a priority pair, with respect to other pairs suchas Gallurese-Corsican, English-Corsican or Spanish-Corsican. In this context, any endangered language has its own priority pair. For example, a priority pair for sardinian gallurese is Italian-Gallurese. In the same way, a priority pair for sardinian sassarese is Italian-Sassarese. In an analogous way, a priority pair for sicilian language is Italian-Sicilian.
A jeweler examines an emerald. “Aha,” he says, “another green emerald. In all my years in this business, I must have seen thousands of emeralds, and every one has been green.” We think the jeweler reasonable to hypothesize that all emeralds are green. Next door is another jeweler having equally comprehensive experience with emeralds. He speaks only the Choctaw Indian language. Color distinctions are not as universal as might be thought. The Choctaw Indians made no distinction between green and blue—the same words applied to both. The Choctaws did make a linguistic distinction between okchamali, a vivid green or blue, and okchakko, a pale green or blue. The Choctaw-speaking jeweler says: All emeralds are okchamali. He maintains that all his years in the jewelry business confirm this hypothesis. (William Poundstone, Labyrinths of reason)