Tag Archives: rule-based machine translation

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.


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).

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.


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

Semantic disambiguation of French ‘femme’: in the mud, gold is still shining

In Corsican language, French word ‘femme’ can be translated, depending on the context

  • either into donna (woman)
  • or into moglia (wife)

The above sample still contains a lot of vocabulary and grammatical disambiguation errors (easy/medium difficulty), but it handles successfully the semantic disambiguation (hard) of ‘femme’, two instances of which are properly translated into moglia (wife). As the Corsican proverb says, in a cianga l’oru luci sempri (in the mud, gold is still shining).

French samples are from the French corpora of the University of Leipzig.

What are the conditions for a given endangered language to be a candidate for rule-based machine translation?

What are the conditions for a given endangered language to be a candidate for rule-based machine translation? For a given endangered language to be a candidate for rule-based machine translation, some requirements are in order. There is notably need for:

  • a dictionary: some specialized lexicons are useful too
  • a list of locutions and their translation: to be more accurate what is needed are noun locutions, adjective locutions, adverbial locutions, verbal locutions and their translations in other language.
  • a detailed grammar (in any language): ideally, the grammar should be very detailed, mentioning notably irregular verbs, noun plurals, etc. Subjonctive, conditional tenses must also be accurately described.
  • in addition, elision rules, euphony rules, should also be described.
  • most importantly: a description of the main variants of the language and their differences. This is needed to handle what we can call the ‘variant problem’ (we shall say a bit more about this later): as an effect of diversity, endangered languages are often polynomic and come with variants. But translation must be coherent and a mix of several variants is not acceptable as a translation.

Let us mention that endangered languages are commonly associated with another language, being in a diglossia relationship one with another. To take an example, Corsican language is associated with French. So we consider the French-Corsican pair, and what is relevant is a French-Corsican. If we consider the sardinian gallurese language (‘gaddhuresu’), the relevant pair is Italian-Gallurese. Other relevant pairs are:

  • Italian-Sassarese
  • Italian-Sicilian
  • Italian-Venetian

How rule-based and statistical machine translation can help each other

Here are a few suggestions on how rule-based and statistical machine translation  can help each other:

To begin with, rule-based and statistical machine translation are often contrasted and compared: it would be oversimplifying to conclude that one is better than the other. From a more objective standpoint, let us consider that each method has its strengths and weaknesses. Let us investigate on how one could make them collaborate in order to add up their respective strengths

in the case of an endangered language, the lack of good quality corpora has been pointed out. But one way for rule-based and statistical machine translation to collaborate would be to use rule-based translation for building a better quality corpus for statistical machine translation

suppose we begin with a statistical machine translation software that performs 50% on average with regard to French to Corsican translation

let us sketch the process of creating these better corpora: let us take the example of the French-Corsican diglossic pair (the Corsican language being considered by Unesco as a definitely endangered language). Now presently we lack a quality French-Corsican corpus or to say it more accurately, the corpus at our disposal is a low-quality one. The idea would be to use rule-based machine translation to create a much better corpus to use with statistical machine translation.

let us sketch now the different steps of this collaborative process: (i) create a French-Corsican corpus with the help of rule-based machine translation: if the software has some average 90% performance, then the corpus would be on average 90% reliable. With appropriate training, statistical MT should now perform some, say, 80% on average (to be compared with the previous 50% performance)
(ii) from this French-Corsican corpus, other corpora pairs can be created, such as Italian-Corsican, English-Corsican, etc. since French-Italian, English-Italian, etc. corpora of excellent quality already exist. The performance gain should then extend to other language pairs such as Italian-Corsican, English-Corsican, etc.

with the help of this process, we re finally in a position to combine and add up the strengths of the two complementary approaches to MT: on the one hand, rule-based MT is able to translate with good accuracy even in the lack of corpora; on the other hand, statistical machine translation is able to handle successfully and fastly a great many language pairs. To sum up, as the Corsican proverb says: una mani lava l’altra (One hand washes the other).