Category Archives: Semantics: blog

Priority pairs for endangered languages

Let us discuss the question of priority pairs with regard to endangered languages. It consists of the most wanted translation pairs for a given endangered language, in keeping with the main language with which it is associated. To take an example: French-Corsican is the priority pair for Corsican language. In the same way, Italian-Gallurese is the priority pair for Gallurese language, etc. Now expanding on that idea, priority pairs are:

  • Corsican: (i) French-Corsican (ii) Italian-Corsican (iii) English-Corsican
  • Sardinian Gallurese: (i) Italian-Gallurese (ii) English-Gallurese
  • Sardinian Sassarese: (i) Italian- Sassarese (ii) English-Sassarese
  • Sardinian Logodurese: (i) Italian-Logodurese (ii) English-Logodurese
  • Sicilian: (i) Italian- Sicilian (ii) English-Sicilian
  • Manx: (i) English-Manx
  • Munegascu: (i) French-Munegascu (ii) Italian-Munegascu (iii) English-Munegascu

More on two-sided grammatical analysis

Let us give some further examples of two-sided grammatical analysis:

  • “à dessein” (purposedly), “à volonté” (at will), “à tort” (mistakenly): from an analytical standpoint, these are prepositions followed by a singular noun. From a synthetical viewpoint, they are adverbs (adverbial locutions).
  • “à jamais” (forever): from an analytical standpoint, it is a preposition followed by an adverb. From a synthetical viewpoint, it is an adverb (adverbial locution).
  • “à genoux” (on my/his/her/… knees), “à torrents” (in torrents): from an analytical standpoint, these are prepositions followed by a plural noun. From a synthetical viewpoint, they are adverbs (adverbial locutions).

Two-sided grammatical analysis

Let us call two-sided grammatical analysis the type of grammatical analysis that will be described below. Two-sided grammatical analysis contrasts with one-sided analysis, which sees a sequence of words either as a locution type (adverbial locution, verbal locution, noun locution, etc.) or as the sequence of types of it constituent words. From the standpoint of two-sided grammatical analysis, a given sequence of words can be attributed one (synthetically) single type, and (analytically) several grammatical types corresponding one-by-one to its constituent words. The upshot is that a given sequence of words can be described from two – synthetic & analytic – different viewpoints. What is now the status of ‘de fait’, from the viewpoint of ‘two-sided grammatical analysis’? From a synthetic standpoint, it is an adverb. And from an analytic viewpoint, it is made up of one preposition (‘de’) followed by a common noun (‘fait’). Both viewpoints are complementary and cast each light on one facet of the same reality. (lacking the time to write a scholar article, but I hope the main idea should be clear…)

A hard case for disambiguation: polymorphic disambiguation

Let us investigate an issue that relates to disambiguation. It is a hard case that needs to be addressed: I shall call it in what follows, for reasons that will become clearer later, polymorphic disambiguation. Let us take an example. It relates to the translation of the two consecutive words: ‘de fait’. The first French sentence ‘De fait, il part.’ translates into Difatti, parti‘ (Actually, he’s leaving.): in this case, ‘de fait’ is considered as an adverbial locution. The second French sentence ‘Il n’y a rien de fait. translates correctly into Ùn ci hè nienti di fattu. (There is nothing done.) where ‘fait’ is now identifed as a participe. The instance at hand concerns French to Corsican, but it should be clear that it arises in the same way within French to English translation. To sum up: the two consecutive words ‘de fait’ can be identifed either as an adverbial locution, or as a preposition (‘de’) followed by a participe (‘fait’, done).

Now we are in a position to formulate the problem in a more general way. It concerns two or more consecutive words, that may be grammatically interpreted differently in the sentence and that may, thus, be translated in a different way. Generally speaking, disambiguation may concern one word (in most cases) but also a group of words. Now polymorphic disambiguation relates then to a given groups of words, i.e. sequences of 2-words, 3-words, 4-words, etc.

A try with online translators shows that statistical MT does better with polymorphic disambiguation. That is truly an interesting difference. So it is a gap that should be filled for rule-based MT.

Some ethics for MT related to endangered languages

Let us sketch what could be some ethical requirements related to machine translation regarding endangered languages.

  • Perhaps a first requirement would be: don’t publish translation pairs regarding an endangered language until the success rate has reached at least 90%. Because instead of helping, it could harm the endangered language in question. For some people could publish these low quality translations, which could have the effect of depreciating the concerned endangered language. There is probably room for discussion here. For even below 90%, some translators could be helpful to some people. But to the very least, it could be suggested that a MT for a given endangered language should display its current success rate.
  • Another point that relates to ethics regarding endangered languages, could be the need for preserving the diversity that is inherent to a given endangered language. For most of them come in variants. Accordingly, we should take into account the main variants of endangered languages, and provide, as far as possible, translations into these main variants. There is recursivity of some kind in this process: if we are to enhance endangered languages in order to preserve language diversity, we should also take into account that diversity when concerned with a single language.

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.

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More on grammatical type disambiguation

Let us focus on grammatical type disambiguation, which is a subproblem of word disambiguation. General grammatical types are: verbs, nouns, adjectives, adverbs, prepositions, gerundive, etc. But for grammatical type disambiguation purposes, more accuracy is in order: instances of grammatical types are then: masculine singular noun, feminine singular noun, masculine plural noun, feminine plural noun, masculine singular adjective, feminine singular adjective, masculine plural adjective, feminine plural adjective, adverbs, prepositions, gerundive, etc. Now grammatical type disambiguation can occur between two different grammatical types (in the above-mentioned form). For example, an ambiguity can occur between preposition and gerundive. In French, this is notably the case for ‘devant’ and ‘maintenant’. For ‘devant’ can either be an adverb (in front) or a gerundive (from the verb ‘devoir’, to have to). Similarly, ‘maintenant’ can either be an adverb (now) or a gerundive (from the verb ‘maintenir’, to maintain). It should be clear now that ‘devant’ and ‘maintenant’ are both ambiguous with regard to their grammatical type. In English, depending on the relevant grammatical type, ‘devant’ is ambiguous between having to or in front). In the same way, ‘maintenant’ is ambiguous between now and maintening.
In order to disambiguate French words ‘devant’ or ‘maintenant’, rule-based MT needs a disambiguation module that is able to distinguish whether ‘devant’ or ‘maintenant’ are adverbs or gerundives.

(not to mention the fact that ‘devant’ can also be a preposition, for the sake of clarity).

New insight on the issue of pair reversal (updated)

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.

How to translate ‘Cette phrase est en français’ ? (This sentence is in French) – updated

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.

What is required from Artificial General Intelligence with regard to Machine Translation?

Illustration from www.pixabay.com

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).

Follow-up to the ‘issue of pair reversal’ and first steps for Italian to Corsican

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.

Superintelligent machine translation (updated)

Illustration from pixabay.com

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.

Also relevant in this discussion is the fact, previously argued, that rule-based translation is better suited to endangered langages translation than statistic-based translation. Why? Because high-scale corpora do not exist for endangered languages. From the above definition of SMT, it follows that rule-based translation is also best suited to SMT, since it massively includes endangered languages (but arguably, statistic-based MT could still be used for translating main languages one into another).

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.

 

Minor breakthrough

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).

Priority pairs regarding endangered languages

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.

Should machine translation software go open source

There is an ongoing debate on whether AI software should go open source or not (for example Bostrom’s paper Strategic Implications of Openness in AI Development). Now our current concern is of whether MT software should go open source or not. Prima facie, for safety reasons, it would be better to render public MT code, thus allowing anyone to check the code and find eventual errors, … Such openness would notably be a defense against the AI control problem , in short, the fact that superintelligence could harm humans. From this standpoint, it seems that publicness of code is much better than privateness. Regarding rule-based translation (the distinction between statistical and rule-based MT is not as clear-cut as one could think at first glance, since some rules could be applied on a statistical basis), it would allow people to check step-by-step the resulting translation. It seems better transparency should be attained accordingly.

Illustration from pixabay.com

Another advantage or publishing the code would be to allow anyone to improve it and extend its capabilities, notably by adding new modules targeted at new languages (human languages’ count being around 7000).

Some thoughts on the remaining 1% problem

To begin with, let us state the 1% problem, for machine translation: it seems some 99% accuracy in machine translation could be attainable but the remaining 1% (1% is just a given number, somewhat arbitrarily chosen, but useful to to fix ideas) may be hard of even very hard to reach. Now a question arises: is some progress on the remaining 1% problem attainable without general-purpose AI. Prima facie, the answer is no. For it seems that progress on the remaining 1% problem requires, for example, some abilities such as being able to find the translation of a given word on external databases. For it will occur sometimes that the 1% untranslated will be due to the presence of a new word, for instance very recently created, and thus lacking in the MT internal dictionary. In order to find the relevant translated word, the machine should be able to search and find it on external databases (say, the web), just as a human would do. So, solving the remaining 1% problem requires – among other capabilities – any such ability which is part of a general-purpose AI.

Illustration from Pixabay.com

Artificial general intelligence (AGI) is prima facie a somewhat abstract notion, that needs to be refined and made more explicit. Problems encountered in implementing machine translation systems can help make this notion more accurate and concrete. The ability to find the translation of a given word on external databases is just one of the required abilities needed to solve the remaining 1% problem. So we shall mention some other abilities of the same type later.

Is rule-based MT more ethical than statistical MT?

In the ongoing debate on safe IA, it is a relevant open question of whether rule-based MT is more ethical than statistical MT. Here are some arguments in favor of rule-based MT in this context (without blaming statistical MT which has its own strengths):

  • it emulates human reasoning: it translates a text just as a human would do
  • there is much control on rule-based MT since the resulting translated text can be traced back: a detailed step-by-step translation process can be provided if required
  • rule-based MT can be consistently part of and integrate itself into a whole project of brain emulation, which emulates general human reasoning

A specific kind of superlative

Let us consider a specific kind of superlative. Such form specific to Corsican language is notably mentioned by grammarian and author Santu Casta, in his  Punteghju, who recommends the following translation of “C’était le village le plus riche du canton” (It was the richest village of the canton):  Era u più paese riccu di stu cantone (pages 26 & 54-55). The structure is original in the sense that the comparative (più) precedes the noun (campanile, bell tower) that precedes the adjective (altu, high).