MACHINE TRANSLATION; HISTORY, PROBLEMS AND PRACTICE

 

BY: Olatz Garcia

Kimetz Pujana

Nuria Sagaley

Tatiana Vegas

 

ABSTRACT

In this report, made for our course on English Language and New Technologies, we are going to focus on Machine Translation. We will make use of some of the Translators we can find in the net and then explain the results we have found. One of our intention is to show a chart with different expressions and Spanish proverbs in both languages, English and Spanish, to make clear the problems resulting from the literary translation.

INTRODUCTION

One of the most useful new technologies developed is Machine Translation .The fact that in our current society the importance of learning new languages has become indispensable, makes the simultaneous machine translation an important tool. Through this report we want to show you the problems we may face with the translators offered freely in the net. Those translators are really useful for people like us, who study degrees where the language is fundamental. We may take into account this errors the translators can bring to make a good job.

We are going to structure this report by means of some charts where we will show how the translators we have find work. In the first of the charts we are going to make the translation from Spanish to English. Then in another chart we will put the translation of the sentence we have get in English into Spanish; through this we want to show how the translators don´t always work and translate things as they were supposed to have been translated.

1· DEFINITION OF MACHINE TRANSLATION

The term machine translation (MT) is normally taken in its restricted and precise meaning of fully automatic translation. However, in this chapter we consider the whole range of tools that may support translation and document production in general, which is especially important when considering the integration of other language processing techniques and resources with MT. We therefore define Machine Translation to include any computer-based process that transforms (or helps a user to transform) written text from one human language into another. We define Fully Automated Machine Translation (FAMT) to be MT performed without the intervention of a human being during the process. Human-Assisted Machine Translation (HAMT) is the style of translation in which a computer system does most of the translation, appealing in case of difficulty to a (mono- or bilingual) human for help. Machine-Aided Translation (MAT) is the style of translation in which a human does most of the work but uses one of more computer systems, mainly as resources such as dictionaries and spelling checkers, as assistants.

 

Traditionally, two very different classes of MT have been identified. Assimilation refers to the class of translation in which an individual or organization wants to gather material written by others in a variety of languages and convert them all into his or her own language. Dissemination refers to the class in which an individual or organization wants to broadcast his or her own material, written in one language, in a variety of language to the world. A third class of translation has also recently become evident. Communication refers to the class in which two or more individuals are in more or less immediate interaction, typically via email or otherwise online, with an MT system mediating between them. Each class of translation has very different features, is best supported by different underlying technology, and is to be evaluated according to somewhat different criteria.

http://sirio.deusto.es/abaitua/konzeptu/nlp/Mlim/mlim4.html

 

 

 

2· TOOLS FOR TRANSLATORS

 

It is now evident that MT as such is not appropriate for translators. They do not like subservience to a machine; they do not want to revise the poor quality of MT systems. What they want are sophisticated translation tools, e.g. translation workstations, which can make their work more productive without taking away the intellectual challenge of translation. What professional translators need are tools to assist them to translate: access to dictionaries and terminological databanks, multilingual word processing, management of glossaries and terminology resources, input and output communication (e.g. OCR scanners, electronic transmission, high-class printing). For these reasons, the most appropriate and successful developments of the last few years have been the translator workstations.

 

The development of translation tools became feasible only since the 1960s, firstly with the availability of real-time interactive computer environments, then the appearance of word processing in the 1970s and of microcomputers in the 1980s and, subsequently, networking and larger storage capacities. From the computational linguistics perspective, the development of translation tools and translation workstations is not as challenging as MT itself for those with 'perfectionist' inclinations, and indeed this research has taken place primarily in non-academic environments.

 

With the appearance of workstations which have appreciably aided the day-to-day work of professional translators, there are clear signs that the previous antagonism of translators to the MT community in general is disappearing. These tools are seen to be the direct result of MT research. The most recent addition has been the `translation memory' facility which enables the storage of and access to existing translations for later (partial) reuse or revision or as sources of example translations; and this facility derives directly from what was initially 'pure' research on bilingual text alignment within corpus-based MT (see below).

http://sirio.deusto.es/abaitua/konzeptu/nlp/Mlim/mlim4.html

 

3·Where We Were Five Years Ago

Machine Translation was the first computer-based application related to natural language, starting after World War II, when Warren Weaver suggested using ideas from cryptography and information theory. The first large-scale project was funded by the US Government to translate Russian Air Force manuals into English. After a decade of initial optimism, funding for MT research became harder to obtain in the US. However, MT research continued to flourish in Europe and then, during the 1970s, in Japan. Today, over 50 companies worldwide produce and sell translations by computer, whether as translation services to outsiders, as in-house translation bureaux, or as providers of online multilingual chat rooms. By some estimates, MT expenditure in 1989 was over $20 million worldwide, involving 200—300 million pages per year (Wilks 92).

 

Ten years ago, the typical users of machine translation were large organizations such as the European Commission, the US Government, the Pan American Health Organization, Xerox, Fujitsu, etc. Fewer small companies or freelance translators used MT, although translation tools such as online dictionaries were becoming more popular. However, ongoing commercial successes in Europe, Asia, and North America continued to illustrate that, despite imperfect levels of achievement, the levels of quality being produced by FAMT and HAMT systems did address some users’ real needs. Systems were being produced and sold by companies such as Fujitsu, NEC, Hitachi, and others in Japan, Siemens and others in Europe, and Systran, Globalink, and Logos in North America (not to mentioned the unprecedented growth of cheap, rather simple MT assistant tools such as PowerTranslator).

 

In response, the European Commission funded the Europe-wide MT research project Eurotra, which involved representatives from most of the European languages, to develop a large multilingual MT system (Johnson, et al., 1985). Eurotra, which ended in the early 1990s, had the important effect of establishing Computational Linguistics groups in a several countries where none had existed before. Following this effort, and responding to the promise of statistics-based techniques (as introduced into Computational Linguistics by the IBM group with their MT system CANDIDE), the US Government funded a four-year effort, pitting three theoretical approaches against each other in a frequently evaluated research program. The CANDIDE system (Brown et al., 1990), taking a purely-statistical approach, stood in contrast to the Pangloss system (Frederking et al., 1994), which initially was formulated as a HAMT system using a symbolic-linguistic approach involving an interlingua; complementing these two was the LingStat system (Yamron et al., 1994), which sought to combine statistical and symbolic/linguistic approaches. As we reach the end of the decade, the only large-scale multi-year research project on MT worldwide is Verbmobil in Germany (Niemann et al., 1997), which focuses on speech-to-speech translation of dialogues in the rather narrow domain of scheduling meetings.

 

http://sirio.deusto.es/abaitua/konzeptu/nlp/Mlim/mlim4.html

4·MAJOR METHODS TECNIQUES AND APPROACHES

Statistical vs. Linguistic MT

One of the most pressing questions of MT results from the recent introduction of a new paradigm into Computational Linguistics. It had always been thought that MT, which combines the complexities of two languages (at least), requires highly sophisticated theories of linguistics in order to produce reasonable quality output.

 

As described above, the CANDIDE system (Brown et al., 1990) challenged that view. The DARPA MT Evaluation series of four MT evaluations, the last of which was held in 1994, compared the performance of three research systems, more than 5 commercial systems, and two human translators (White et al., 1992—94). It forever changed the face of MT, showing that MT systems using statistical techniques to gather their rules of cross-language correspondence were feasible competitors to traditional, purely hand-built ones. However, CANDIDE did not convince the community that the statistics-only approach was the optimal path; in developments since 1994, it has included steadily more knowledge derived from linguistics. This left the burning question: which aspects of MT systems are best approached by statistical methods, and which by traditional, linguistic ones?

 

Since 1994, a new generation of research MT systems is investigating various hybridisations of statistical and symbolic techniques (Knight et al., 1995; Brown and Frederking, 1995; Dorr , 1997; Nirenburg et al., 1992; Wahlster, 1993; Kay et al., 1994). While it is clear by now that some modules are best approached under one paradigm or the other, it is a relatively safe bet that others are genuinely hermaphroditic, and that their best design and deployment will be determined by the eventual use of the system in the world. Given the large variety of phenomena inherent in language, it is highly unlikely that there exists a single method to handle all the phenomena--both in the data/rule collection stage and in the data/rule application (translation) stage--optimally. Thus one can expect all future non-toy MT systems to be hybrids. Methods of statistics and probability combination will predominate where robustness and wide coverage are at issue, while generalizations of linguistic phenomena, symbol manipulation, and structure creation and transformation will predominate where fine nuances (i.e., translation quality) are important. Just as we today have limousines, trucks, passenger cars, trolley buses, and bulldozers, just so we will have different kind of MT systems that use different translation engines and concentrate on different functions.

 

 

One way to summarize the essential variations is as follows:

 

* Feature Symbolic Statistical

 

robustness/coverage: lower higher

quality/fluency: higher lower

representation: deeper shallower

 

How exactly to combine modules into systems, however, remains a challenging puzzle. As argued in (Church and Hovy, 1993), one can use MT function to identify productive areas for guiding research. The `niches of functionality’ provide clearly identifiable MT goals. Major applications include:

 

assimilation tasks: lower quality, broad domains – statistical techniques predominate

dissemination tasks: higher quality, limited domains – symbolic techniques predominate

communication tasks: medium quality, medium domain – mixed techniques predominate

 

Ideally, systems will employ statistical techniques to augment linguistic insights, allowing the system builder, a computational linguist, to specify the knowledge in the form most convenient to him or her, and have the system perform the tedious work of data collection, generalization, and rule creation. Such collaboration will capitalize on the (complementary) strengths of linguist and computer, and result in much more rapid construction of MT systems for new languages, with greater coverage and higher quality. Still, how exactly to achieve this optimal collaboration is far from clear. Chapter 6 discusses this trade-off in more detail.

 

http://sirio.deusto.es/abaitua/konzeptu/nlp/Mlim/mlim4.html

5· PRACTICAL MACHINE TRANSLATION

 

All current commercial and operational systems and probably most future ones produce output which must be edited (revised) if it is to be of publishable quality. Only if rough translation is acceptable for information-gathering purposes can the output of MT systems be left untouched by human revisers. It follows that commercial developers of MT must provide adequate facilities for the revision of texts. They must also stress to customers that MT does not and cannot produce translations acceptable without revision. It is a lesson which was learnt, painfully in some cases, during the 1980s and most, perhaps all, current MT developers and system vendors stress the imperfect nature of MT output.

 

MT systems have been developed on the assumption that they would be used primarily by bilinguals. In practice, the post-editing of MT output has been given to people knowing both the source and the target languages, i.e. most often to professional translators. It is a practice which has damaged the image of MT among translators: they do not wish to be revisers of poor quality output from a machine. In more recent years, the training of bilingual staff specifically for post-editing has been advocated and successfully implemented in many MT operations. The lesson has been learnt that MT post-editing should not be imposed upon professional translators; it is better to train people specifically for this role.

 

It is now widely accepted that MT works best in domain-specific and controlled environments. In this respect, MT developers have effectively taken up the themes and suggestions first propounded by the pioneers in the 1950s. Sub language systems were also an early proposal - in the form of micro glossaries - and since the success of Meteo in the mid 1970s have remained the focus of research systems. The control of language was in the rather crude and easily dismissible form of highly simplified 'model English' (Dodd 1955). The idea was largely forgotten until the practical application of Systran by Xerox in the late 1970s. Other applications of controlled input followed with the available general-purpose systems. Now it is recognised that MT systems should be designed ab initio for controlled language, and a number of independent companies outside the academic MT research community have been doing so in recent years (e.g. Volmac). The largest current development is the Caterpillar project based on the research at Carnegie Mellon University.

 

One of the most important lessons from the history of MT research is that there can be no quick results. The development of operational systems is a long term commitment. Even good working prototypes with reasonably large dictionaries and many years of testing cannot be easily scaled up for fully reliable operational installation. Consequently no commercial system can incorporate the very latest methodology and technology, and this fact should be made more clearly to potential purchasers. The emphasis should be on reliability and economic viability.

 

The construction and compilation of dictionaries is essential to the success of any and all MT systems. But there are no easy solutions. The adaptation of conventional dictionaries to the needs of MT is not trivial. Much attention was paid to dictionaries in the 1950s and 1960s - indeed the 'direct translation' approach required most selectional and structural information to be accessed from lexical entries. In the years following, problems of syntactic analysis and structural transfer dominated research and the lexicon was relatively neglected for many years. Since the mid 1980s, with the adoption of constraint-based and unification grammatical formalisms there has consequently been a more 'lexicalist' orientation, and the lexicon has become again the focus of much current MT research. It is to be hoped that this research will both accelerate the development of MT prototypes and improve the operational flexibility of commercial systems.

http://sirio.deusto.es/abaitua/konzeptu/nlp/Mlim/mlim4.html

 

6· RESEARCH METHODS FOR MACHINE TRANSLATION

 

Researc h systems have often been developed without any idea of how they might be used or who the users might be. In many cases, researchers have begun with the intention of exploring the potential of a single theory, method or technique. It has often been the case that possible practical use is considered only after a prototype system has been built and evaluated and its operational limitations have been defined. It can be argued that the relationship should if anything be reversed, with research as the hand-maiden of practical MT.

 

What is forgotten by many is that MT is not a theoretical science; it is the application of computational, linguistic, etc. methods and techniques to a practical task. Translation is itself a means to an end: the communication of a message or information in a language other than that it was originally composed. It is a task which has never been and cannot be 'perfect'; there are always the possibilities of multiple translations of the same text or message according to different circumstances and requirements. MT is no different: there cannot be a 'perfect' automatic translation. The use of an MT system is contingent upon its cost effectiveness in practical situations.

 

Nevertheless, MT research continues to attract the perfectionists. It has been regarded as a field in which new linguistic formalisms or new computational techniques can be tried out: MT has been seen as a test bed for theories. The reason is obvious: the quality of MT and translation can be judged by non-experts, at least in a superficial manner - reliable and systematic evaluation is quite another matter.

 

The list of such applications of 'external' theories is long. It began in the 1950s and 1960s with information theory, categorial grammar, transformational-generative grammar, dependency grammar, and stratificational grammar. In the 1970s and 1980s came MT research based on artificial intelligence, non-linguistic knowledge bases, formalisms such as Lexical-Functional Grammar, Generalized Phrase Structure Grammar, Head-driven Phrase Structure Grammar, Definite Clause Grammar, Principles and Parameters, Montague semantics. In the 1990s have been added neural networks, connectionism, parallel processing, and statistical methods, and many more.

 

In nearly every case, it has been found that the 'pure' adoption of the new theory was not as successful as initial trials on small samples appeared to demonstrate. Inevitably the theory had to be adapted to the demands of MT and translation, and in the process it became modified. But innovativeness and idealism must not to be discouraged in a field such as MT where the major problems are so great and all promising approaches must be examined closely. Unfortunately, there has been a tendency throughout the history of MT for the advocates of new approaches to exaggerate their contribution. Many new approaches have been proclaimed as definitive solutions on the basis of small-scale demonstrations with limited vocabulary and limited sentence structures. It is these initial untested claims that must always be treated with great caution. This lesson has been learnt by most MT researchers; no longer do they proclaim imminent breakthroughs.

 

The history of MT research has gone through a number of phases in which certain frameworks have dominated. From the late 1960s the syntactic orientation was dominant, initially with syntactic transfer approaches (e.g. at MIT), then the interlingua formalisms of CETA and LRC, followed by the "second generation" transfer-based multi-level model of GETA-Ariane, SUSY, Mu, and Eurotra. In the 1980s the AI orientation was popular (e.g. Carnegie Mellon), more attention was paid to semantics and interlingua-based systems were explored (e.g. Rosetta and DLT). And now in the 1990s, the corpus-based paradigm with stochastic and example-based methodologies is the focus of much activity.

 

Thus we see the rise and fall and subsequent revival of methods and approaches. Statistics-based MT disappeared for almost thirty years between the early 1960s and the IBM project Candide at the end of the 1980s. The interlingua idea has also had periods of neglect: Weaver's suggestion in 1949 was not taken up until the late 1950s by researchers in Cambridge, in Moscow and Leningrad, and when valuable theoretical research was undertaken; it flourished for a while in the syntactic interlinguas of Grenoble and Texas, and then for a decade it was considered too ambitious and the transfer-based approach was preferred until interlingua system came back again in the mid-1980s.

 

The lesson to be derived from such fluctuating fortunes is that no old or unfashionable theory or approach should be disregarded simply because it has once been found inadequate. MT has a long history, longer that many of those who have only recently entered the field are often aware of. Before applying some new approach to MT on a large scale, researchers and their funders should assure themselves that previous work is not about to be replicated

The advantage of this long experience is that there are many old wheels which do not have to be reinvented. There are large areas of morphological and syntactic analysis which can be adopted successfully by any new system. Indeed the success of many custom-built systems in recent years demonstrates that methods of MT and of computational linguistics are becoming widely known outside the narrow research community and can be applied with success in working MT systems.

http://sirio.deusto.es/abaitua/konzeptu/nlp/Mlim/mlim4.html

7· PROBLEMS WITH MACHINE TRANSLATION

 

Machine translation works quite well for translating predictable technical texts – texts which never go beyond the expected domain of discourse. But this is little help in the domains where people want translation the most: for spontaneous conversations, in person, on the telephone, and on the Internet.

Computers just do not have the ability to deal adequately with the various complexities of language than humans handle naturally: ambiguity, syntactic irregularity, multiple word meanings and the influence of context. A classic example is illustrated in the following pair of sentences: 

Time flies like an arrow.
Fruit flies like an apple.

The sentence construction is parallel, but the meanings are entirely different: the first is a figure of speech involving a metaphor and the second is a literal description. And the identical words in the sentences - flies and like - are used in different grammatical categories. A computer can be programmed to understand either of these examples, but not to distinguish between them. 

A computer translation is similar to a translation done by a human without a deep knowledge of the target language. Grammatical rules can be memorised, or programmed. But without real knowledge of a language, a human or a computer simply looks up words in a dictionary and has no way to select between alternate meanings. Alan Melby, professor of linguistics at Brigham Young University, points out that "Being a native or near-native speaker involves more than just memorizing lots of facts about words. It includes having an understanding of the culture that is mixed with the language. It also includes an ability to deal with new situations appropriately. No dictionary can contain all the solutions since the problem is always changing as people use words in unusual ways." ("Why Can’t a Computer Translate More Like a Person?")

Another classic example of the difficulties of MT was provided in 1960 by Bar-Hillel, an early machine translation researcher. With the seemingly simple sentence The box is in the pen he pointed out "that to decide whether the sentence is talking about a writing instrument pen or a child's play pen, it would be necessary for a computer to know about the relative sizes of objects in the real world… The point is that accurate translation requires an understanding of the text, which includes an understanding of the situation and an enormous variety of facts about the world in which we live." Computers cannot translate like humans because they do not learn like humans. (Alan Melby, "Why Can’t a Computer Translate More Like a Person?")

Silberman quotes Martin Kay, an MT developer: MT is an 

"AI-complete problem." You have to solve all of the various difficulties of imbuing computers with the kind of knowledge that humans naturally harvest from experience before you can tackle the essential problem of MT. "When you want to hire a translator," Kay explains, "you ask, ‘How good is your Chinese? How good is your French?’ You don't ask, ‘Have you been around much in the world? The problem is, machines haven't. In order to understand a sentence, your knowledge of linguistics is a relatively minor matter. Your knowledge of the world is incredibly important." ("Talking to Strangers," Wired, May 2000)

Computers not only lack the knowledge of the world to deal with word choice, but they also lack the knowledge necessary for cultural sensitivity. Melby writes that translation needs to be "sensitive to total context, including the intended audience of the translation. Meaning is not some abstract object that is independent of people and culture." As an example of the damage that can be done by culturally ignorant and insensitive translation, even by humans, he describes his investigation of the translation of a remark made by Nikita Khrushchev in Moscow on November 19, 1956:

Khrushchev was then the head of the Soviet Union and had just given a speech on the Suez Canal crisis. Nassar of Egypt threatened to deny passage through the canal. The United States and France moved to occupy the canal. Khrushchev complained loudly about the West. Then, after the speech, Khrushchev made an off-hand remark to a diplomat in the back room. That remark was translated "We will bury you" and was burned into the minds of my generation as a warning that the Russians would invade the United States and kill us all if they thought they had a chance of winning…Several months ago, I became curious to find out what Russian words were spoken by Khrushchev and whether they were translated appropriately…In Soviet Communist rhetoric, it is common to claim that history is on the side of Communism, referring back to Marx who argued that Communism was historically inevitable. Khrushchev then added that Communism does not need to go to war to destroy Capitalism. Continuing with the thought that Communism is a superior system and that Capitalism will self-destruct, he said, rather than what was reported by the press, something along the lines of ‘Whether you like it or not, we will be present at your burial,’ clearly meaning that he was predicting that Communism would outlast Capitalism. Although the words used by Khrushchev could be literally translated as "We will bury you," (and, unfortunately, were translated that way) we have already seen that the context must be taken into consideration. The English translator who did not take into account the context of the remark, but instead assumed that the Russian word for "bury" could only be translated one way, unnecessarily raised tensions between the United States and the Soviet Union and perhaps needlessly prolonged the Cold War. ("Why Can’t a Computer Translate More Like a Person?")

Melby believes that the reason computers cannot translate like humans lies in one factor: their lack of agency. By agency, he means

the capacity to make real choices by exercising our will, ethical choices for which we are responsible… A computer has no real choice in what it will do next. Its next action is an unavoidable consequence of the machine language it is executing and the values of data presented to it…Without agency, information is meaningless. So a computer that is to handle language like a human must first be given agency. But we should be careful, because if we give agency to a computer it may be hard to get it back and the computer, even if it chooses to learn a second language, may exercise its agency and refuse to translate for us. Douglas Robinson (1992) puts it well. He asks whether a machine translation system that can equal the work of a human might not "wake up some morning feeling more like watching a Charlie Chaplin movie than translating a weather report or a business letter." ("Why Can’t a Computer Translate More Like a Person?")

http://www.diplomacy.edu/Language/Translation/machine.htm

 

 

8·CHART I

What we are going to do in this chart is to get the translations of some Spanish proverbs from three different translators taken from the net: "El Mundo Translator", "Systran Translator" and "Babelfish translator". Through this we want to show the amount of possibilities we can find in the net, and how each translator gives us a different meaning.

 

Spanish proverbs

El Mundo translator

Systran translator

Babelfish translator

A buen entendedor pocas palabras bastan

Few words are enough to good one who understands

To good entendedor few words are enough

To good entendedor few words are enough

A cada cerdo le llega su San Martín

To every pork his San Martin comes

At each pig it arrives it san to him Martin

At each pig it arrives its san to him Martin

Por si las moscas

For if the flies

Just in case

Just in case

Cada oveja con su pareja

Every sheep with his pair

Each ewe with its pair

Each ewe with its pair

A quien madruga Dios le ayuda

Whom it forestalls God helps him

To who madruga God him aid

To whom madruga God him aid

Aunque la mona se vista de seda mona se queda

You can´t make a silk purse out of sow’s ear

Although mona silk vista mona remains

Although mona silk vista mona remains

Cría cuervos y te sacarán los ojos

Baby raven and the yes were extracting you

Cria crows and removed the eyes to you

Cria crows and removed the eyes to you

De noche todos los gatos son pardos

By night all the cats are dun

At night all the cats are brown

At night all the cats are brown

Cuando el grajo vuela bajo hace un frío del carajo

When the raven flies down it does a cold of carajo

When the rook flies under does a cold one of the carajo

When the rook flies under does a cold one of the carajo

El mundo es un pañuelo

The world is a handkerchief

The world is a handkerchief

The world is a handkerchief

El más ruin puerco come la mejor bellota

The mean nasty mass eats the best acorn

But ruin filthy eats the best acorn

But ruin filthy eats the best acorn

Más vale pájaro en mano que ciento volando

A bird in the hand is worth two in the bus

But it is worth pajaro in hand that one hundred flying

But it is worth pajaro in hand that one hundred flying

La mujer hermosa o loca o presuntuosa

The beautiful or mad or presumptuous woman

The beautiful or crazy or presumptuous woman

The beautiful woman or presumptuous crazy person or

Mucho ruido y pocas nueces

Much ado about nothing

Much noise and few nuts

Much noise and few nuts

Más sabe el loco en su casa que el cuerdo en ajena

Mas the madman knows in his house that the sane one in the foreign one

But the crazy person in his house knows that the prudent one in other people’s

But it is worth the crazy person in its house that the prudent one in its house

No es aquella gallina buena que come en tu casa y pone en a ajena

It is not that good hen that he eats in your house and puts in the foreign one

It is not that good hen that they copiss your house and puts in the other people’s one

It is not that good hen that it eats in your house and it puts in the other people’s one

No es oro todo lo que reluce

It is not a gold everything what re-shines

Everything is not gold what shines

Everything is not gold what shines

Ojos que no ven corazón que no siente

Eyes that do not see Heart that he does not feel

Eyes that do not see heart that does not feel

Eyes that do not see heart that does not feel

Perro ladrador poco mordedor

Dog ladrador slightly biting

Little mordedor ladrador dog

Little mordedor ladrador dog

Quien fue a Sevilla perdió su silla

The one who was to Seville perdio his chair

Who went to Seville perdio his chair

Who went to Seville perdio his chair

Quien lava la cabeza al asno perdió el jabón y el tiempo

The one who washes the head to the jackass perdio the soap and the time

Who washes to the head to the ass perdio the soap and the time

Who washes the head to the ass, perdio the soap and its time

Mala hierba nunca muere

Bad grass never dies

Bad grass never dies

Bad grass never dies

 

Through this chart we are able to see how some Machine Translators work by means of direct translation. I mean that there are Machine Translators which use the first meaning they have of a word without taking into account the sentence as a whole, and as a consequence of this the translation we get is incoherent with respect to the meaning we need.

One other problem we may face with this machine is that there are words that some translators does not understand or find so they don´t translate them and the result is that the sentence is non-sense.

 

9·CHART II- EL MUNDO TRANSLATOR

In this chart we are going to see how translating into English the results we have get from the same translator in the previous action( from Spanish to English) what we get is not correct in our current language. It´s quite surprising how the same translator gives us a different vercsion from a sentences it has already traduced the other way around.

English sentences

Traduction into Spanish

Few words are enough to good one who understands

Pocas palabras son bastantes a los buenos que entienden

To every pork his San Martin comes

A cada carne de cerdo su San Martín viene a él

For if the flies

Ya que si vuela

Every sheep with his pair

Cada oveja con su par

Whom it forestalls God helps him

Quien esto previene al dios le ayuda

You can´t make a silk purse out of sow’s ear

Aunque la mona se vista de seda mona se queda

Baby raven and the yes were extracting you

Los cuervos del bebé y los ojos le extraían

By night all the cats are dun

Antes de la noche todos los gatos son pardos

When the raven flies down it does a cold of carajo

Cuando el cuervo vuela bajo esto hace un frío del carajo

The world is a handkerchief

El mundo es un pañuelo

The mean nasty mass eats the best acorn

Mas tacaño repugnante come bellotas mejor

A bird in the hand is worth two in the bus

Más vale pájaro en mano que cien volando

The beautiful or mad or presumptuous woman

La mujer Hermosa o loca o presumida

Much ado about nothing

Mucho ruido y pocas nueces

Mas the madman knows in his house that the sane one in the foreign one

Más el loco sabe en su casa que el sano en el extranjero

It is not that good hen that he eats in your house and puts in the foreign one

No es que la gallina Buena que ella come en su casa y ponga en el extranjero

It is not a gold everything what re-shines

No es oro todo que el nuevo brillo

Eyes that do not see Herat that he does not feel

Los ojos que no ven el corazón que él no siente

Dog ladrador slightly biting

Persiga ladrado ligeramente mordiendo

The one who was to Seville perdio his chair

El que que era a Sevilla perdió su silla

The one who washes the head to the jackass perdio the soap and the time

El que que lava la cabeza a un burro perdió el jabón y el tiempo

Bad grass never dies

Mala hierba nunca muere

 

http://www.el-mundo.es/traductor/

 

10· CHART III- SYSTRAN TRANSLATOR

 

Few information on Systran:

Corporate Overview


SYSTRAN is the leading provider of the world's most scalable and modular translation architecture. Its core technology powers revolutionary translation solutions for the Internet, PCs and network infrastructures that facilitate communication in 36 language pairs and in 20 specialized domains.

SYSTRAN's Machine Translation (MT) technology integrates multilingual functionalities in information processing and exchanges, for applications such as eCommerce, CRM, Content Management, databases, corporate Intranets, email, Instant Messaging, SMS, WAP, and more. MT enables the creation of dynamic business content at a fraction of the cost of professional human translation services.

SYSTRAN's expertise spans over 30 years of building customized translation solutions for large corporations, portals, ISPs, governments and public administrations through open and robust architectures.


http://www.systransoft.com/

As we have done with the previous chart we are going to enter in the "systran translator" the results we have get from the translation of the proverbs we have chosen into English and get it into Spanish again, to show you how this translators are not a hundred per cent reliable, although they are really useful sometimes.

English sentences

Traduction into Spanish

To good entendedor few words are enough

Bueno entendedor poco palabra ser bastante

At each pig it arrives it san to him Martin

Cada cerdo llega su san a él Martín

Just in case

Apenas en caso de que

Each ewe with its pair

Cada oveja con su par

To who madruga God him aid

Quien Dios del madruga él ayuda

Although mona silk vista mona remains

Aunque permanece el mona de seda de vista del mona

Cria crows and removed the eyes to you

Cuervos del cría y quitado los ojos a usted

At night all the cats are brown

En la noche todos los gatos son marrones

When the rook flies under does a cold one of the carajo

Cuando las moscas del grajo debajo hacen frío de carajo

The world is a handkerchief

El mundo es un pañuelo

But ruin filthy eats the best acorn

Pero la ruina asquerosa come la mejor bellota

But it is worth pajaro in hand that one hundred flying

Pero el pájaro a disposición que ciento vuela

The beautiful or crazy or presumptuous woman

La mujer hermosa o loca o presumida

Much noise and few nuts

Mucho ruido y pocas tuercas

But the crazy person in his house knows that the prudent one in other people’s

Pero la persona loca en su casa sabe que la prudente en la gente

It is not that good hen that they copiss your house and puts in the other people’s one

No es esa Buena gallina que los copiss s casa ponen en la otra gente una

Everything is not gold what shines

Todo no es oro que brilla

Eyes that do not see heart that does not feel

Ojos que no ven corazón que no siente

Little mordedor ladrador dog

Poco perro de ladrador del mordedor

Who went to Seville perdio his chair

Quien fue al perdió de Sevilla su silla

Who washes to the head to the ass perdio the soap and the time

Quien lava a la cabeza al perdió del asno el jabón y el tiempo

Bad grass never dies

Malos dados de la hierba nunca

http://www.systransoft.com/

 

11·CHART IV- BABELFISH TRANSLATOR

 

This is going to be the third time we make the same operation in this report with the sentences we get from the very first translation we have made. The result are going to be translated again into English to prove the problems the Machine Translation may have.

English sentences

Traduction into Spanish

To good entendedor few words are enough

A buen entendedor pocas palabras son bastantes

At each pig it arrives its san to him Martin

Cada cerdo llega su san a él Martín

Just in case

Apenas en caso de que

Each ewe with its pair

Cada oveja con su par

To whom madruga God him aid

Quién Dios del madruga a él ayuda

Although mona silk vista mona remains

Aunque permanece el mona de seda de Vista del mona

Cria crows and removed the eyes to you

Coronas del cría y quitado los ojos a usted

At night all the cats are brown

En la noche todos los gatos son marrones

When the rook flies under does a cold one of the carajo

Cuando las moscas del grajo debajo hacen frío de carajo

The world is a handkerchief

El mundo es un pañuelo

But ruin filthy eats the best acorn

Pero la ruina asquerosa come la mejor bellota

But it is worth pajaro in hand that one hundred flying

Pero vale el pájaro a disposición que ciento que vuela

The beautiful woman or presumptuous crazy person or

La mujer Hermosa, o persona loca de los presuntuosos o

Much noise and few nuts

Mucho ruido y pocas tuercas

But it is worth the crazy person in its house that the prudent one in its house

Pero vale a persona loca en su casa esa la prudente de su casa

It is not that good hen that it eats in your house and it puts in the other people’s one

No es que los buenos hombres que come en su casa y él pone en la otra gente una

Everything is not gold what shines

Todo no es oro qué brilla

Eyes that do not see heart that does not feel

Ojos que no ven el corazón que no se siente

Little mordedor ladrador dog

Poco perro del ladrador del mordedor

Who went to Seville perdio his chair

Quien fue al perdió de Sevilla su silla

Who washes the head to the ass, perdio the soap and its time

Quien lava la cabeza al asno, al perdió el jabón y a su tiempo

Bad grass never dies

Malos dados de la hierba nunca

http://babelfish.altavista.com

 

11·CONCLUSION

As a conclusion we could say that this "new techniques" recently discovered or invented are really useful for people in general, specially for us a s students of philology. We are suppose to dominate few languages( at least 2) and sometimes we ay find some troubles with the translation of some specific sentences; this Machine Translation is a tool to solve this kind of problem. However, we have to realize that not everything is positive with respect to these new tools, because they have some restrictions as we have briefly mentioned before. One of the most important problems is that of the direct translation or the one related to the word that are left with no translation or corresponding word in the second language.

As you have seen we have made a little experiment in this report in which we re-enter into the translators the sentences we get from the first translation we made into English. Sometimes the result has nothing to do with the sentence we have introduced in the first step of our experiment, we sometimes get really funny meanings from sentences such as the case of the sentence "cria cuervos y te sacaran los ojos", when we translate it into English we get " cria crows and removed the eyes to you" and when we made the last step what we get was a rally non-sense Spanish sentence as " coronas del cria y quitado los ojos a usted".