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The Notion Of Machine Translation English Language Essay

3682 words (15 pages) Essay in English Language

5/12/16 English Language Reference this

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The translation process was mentioned as being one of the most effective that is a means of communication especially among cultures of different languages. Translation as a concept has existed hundred years ago, but it is only during the second half of the twentieth century that it emerged as an independent academic. A terrible need for translation has prompted specialized and theorists in the field to seek for more sophisticated methods and techniques for quick, cheap and effective translation. Thus, a new type of translation has appeared to compete with Human Translation which is called Machine translation or the automatic translation.

Nowadays the use of machine translation is very important than we may think, because different aspects of modern life have direct for more efficient methods of translation, thus the demand for translation is not satisfied, because there are not enough human translators, or because individuals and organizations do not recognize translation as a complex activity requiring a high level of skill, and therefore they are not prepared to pay what it is worth.

This research attempts to compare the most important linguistic aspects of machine translation and to analyze its main problems.

The purpose of the given research is to analyze the difficulties of machine translation.

The hypothesis that we postulate for this research is that the interlingua approach display the greatest degree of difficulty in the process of translation.

The specific objectives of the research are:

to define the notion of Machine Translation;

to identify and compare different machine translation approaches;

to analyze the main problems of machine translation;

The research methods employed in the work are analysis, which was used for the study of machine translation and determining its essential features; diachronic analysis, that focuses on historical development of machine translation; the classification method was used for classifying the strategies of machine translation and their problems of ambiguity.

We chose this topic, because the machine translation is a tool that allows people to have information about a variety of things in different languages and to understand it without knowing the language. Furthermore it permits us to have the meaning of a word or expressions in a rapid and effective way. As well Machine Translation provides translators useful tools that help them to make their job more efficiently and faster.

The most important sources that have been used are: “Concise History of the Language Sciences: from the Sumerians to the cognitivists” by Koemer E.F., “An Introduction to Machine Translation” by W. J. Hutchins and Harold L. Somers, “Introducing Translation Studies: Theories and Applications” by Munday J., “Machine Translation” by Maegaard B., and “Machine Translation: An Introductury Guide” by Arnold D. J,

Language is the major method for people communicating with each other, but people can only communicate each other with language they both know. Unfortunately there are around 7000 different kinds of languages in the world, and these languages may have different writing systems, grammar and pronunciation. On the other hand, the fast grows of international communication (such as international businesses, national diplomacy, and international conferences) making the demand of translation (such as business document translation, legal document translation and scientific and technical documents translation) is also growing rapidly, cheap and fast translations are required. In this case machine translation becomes a solution.

Identifying different definitions of Machine Translation

Machine translation of natural languages, commonly known as MT, has multiple personalities. Sergei Nirenburg and Yorick Wilks, in their book “Machine Translation” claim that,first of all, machine translation is a venerable scientific enterprise, a component of the larger area of studies concerned with the studies of human language understanding capacity.

They write that MT is also a technological challenge of the first order. It offers an opportunity to test the understanding of the syntax and semantics of a variety of languages by encoding this vast, though rarely comprehensive, knowledge into a form suitable for processing by computer programs. Also in this book “Machine Translation” they state that MT has a strong connection with the needs of modern societies. It can be understood as an economic necessity, considering that the growth of international communication keeps intensifying both at government, for instance, European Union, NAFTA, GATT and business and commerce levels, for instance, the exporters need product documentation in the languages of the countries where their products are marketed [12].

In the article “Brief History of Machine Translation Research” Leon Dostert mentions that the story of the genesis of machine translation was traced with care in the first compendium of essays on the subject entitled Machine Translation of Languages, edited by William Lock and A. Donald Booth. In which they write that the transference of meaning from one patterned set of signs occurring in a given culture into another set of patterned signs occurring in another related culture by means of an electronic computer [7].

In the report “Language and Machines Computers in Translation and Linguistics” is stated that machine translation means that it should go by algorithm from machine- readable source text to useful target text, without recourse to human translation or editing [1].

In “An Introduction to Machine Translation” W. John Hutchins and Harold L. Somers explain that the term Machine Translation is the traditional and standard name for computerized systems responsible for the production of translations from one natural language into another, with or without human assistance. Machine translation can be named as mechanical translation and automatic translation. These terms are now rarely used in English, but their equivalents in other languages are used, for example in French traduction automatique, or in Russian автоматический перевод. Also in this book is written that the term does not include computer-based translation tools which support translators by providing access to dictionaries and remote terminology databases, facilitating the transmission and reception of machine-readable texts, or interacting with word processing, text editing or printing equipment, but, however, it includes systems in which translators or other users assist computers in the production of translations, including various combinations of text preparation, on-line interactions and subsequent revisions of output [16].

M.Kay and Xerox Parc in their article “Machines and People in Translation” write that we should distinguish a narrower and a wider use for the term machine translation. In the narrow sense, the term refers to a batch process in which a text is given over to a machine from which a result is collected which is the output of the machine translation process. When we use the term in the wider sense, it includes all the process required to obtain final translation output on paper [8].

In the article “Machine Translation Workstation” is stated that the MT is a general tree-manipulation system with several built-in inference strategies. They demonstrate the process of machine translation through the following scheme:

And they say that when a user applies the machine he/she writes a rule base to control the execution of the machine and chooses the appropriate inference strategy. The machine takes well-defined linguistic trees as input and produces as output trees which represent meaning-preserving transformations of the input trees. Furthermore the MT is language independent, because it impose restrictions on what kinds of transformations are possible [4].

In conclusion we can say that machine translation is an automatic linguistic translation”, namely, a word-by-word translation and it refers to the utilization of software to translate text from one language to another language.

Machine Translation Strategies

In the article “Machine Translation and Computer-Assisted Translation” Craciunescu states that Machine translation is an autonomous operating system with strategies and approaches that can be classified as follows:

the direct strategy

the transfer strategy

the pivot language strategy

She says that the direct strategy is based on a predefined source language-target language binomial in which each word of the source language syntagm is directly linked to a corresponding unit in the target language with a unidirectional correlation, for example from English to Spanish but not the other way round.

But the transfer strategy is based on the level of representation and involves three stages. The analysis stage describes the source document linguistically and uses a source language dictionary. The transfer stage transforms the results of the analysis stage and establishes the linguistic and structural equivalents between the two languages. It uses a bilingual dictionary from source language to target language. The generation stage produces a document in the target language on the basis of the linguistic data of the source language by means of a target language dictionary.

The pivot language strategy is based on the idea of creating a representation of the text independent of any particular language. This representation functions as a neutral that is distinct from both the source language and the target language. This method reduces the machine translation process to only two stages: analysis and generation. The analysis of the source text leads to a conceptual representation, the diverse components of which are matched by the generation module to their equivalents in the target language [5].

Another characterization of strategies of MT we find at W.J. Hutchins and Jonathan Sloculn in their articles “Machine Translation: A Brief History” and Its History, Current Status, and Future Prospects” distinguish three basic strategies.

The first strategy is referred to the direct translation approach. Direct translation is characteristic of a system designed from the start to translate out of one specific language and into another. For example, Russian is the language of the original texts-the source language, and English is the language of the translated texts-the target language. Translation is direct from the source language (SL) text to the target language (TL) text [14].

Arnold in his book “Machine Translation” represents the direct approach through the following scheme[3]:

Text SL

Direct Translation Text TL

The second basic design strategy is the Interlingua approach, which assumes that it is possible to convert SL texts into representations common to more than one language. Furthermore the Interlingua approach is characteristic of a system in which the representation of the meaning of the source language input is intended to be independent of any language, and this representation is used to synthesize the target language output [14].

In his book “Machine Translation” Arnold represents the Interlingua approach through the following scheme [3]:

IL

Analysis

Synthesis

Direct Translation

Text SL Text TL

The third basic strategy is the less ambitious transfer approach. The transfer approach is characteristic of a system in which the underlying representation of the meaning of a grammatical unit (e.g., sentence) differs depending on the language from which it was derived or into which it is to be generated; this implies the existence of a third translation stage which maps one language-specific meaning representation into another: this stage is called Transfer. The transfer approach operates through three stages involving underlying (abstract) representations for both SL and TL texts. The first stage converts SL texts into abstract SL-oriented representations; the second stage converts these into equivalent TL-oriented representations; and the third generates the final TL texts. Whereas the Interlingua approach necessarily requires complete resolution of all ambiguities in the SL text so that translation into any other language is possible, in the transfer approach only those ambiguities inherent in the language in question are tackled; problems of lexical differences between languages are dealt with in the second stage (transfer proper) [14].

Arnold also represents the third approach, the transfer approach, through a scheme as follow [3]:

Analysis IS SL

Transfer ISTL

Synthesis

TEXT SL Text TL

Direct Translation

In brief, the interlingual machine translation is one of the classic approaches to machine translation. In this approach, the source language – the text to be translated is transformed into an interlingua – an abstract language-independent representation. The target language is then generated from the interlingua. Furthermore, the interlingual approach is an alternative to the direct approach and the transfer approach.

Main problems of machine translation

The major problems of all MT systems concern the resolution of lexical and structural ambiguities, both within languages (monolingual ambiguity) and between languages (bilingual ambiguity). The lexical ambiguity is when a word has more than one meaning, but when a phrase or sentence can have more than one structure it is called structural ambiguity [3].

Hutchins in his article “Machine Translation: History and General Principles” mentions that any monolingual ambiguity is a potential difficulty in translation since there will be more than one possible equivalent. For instance, homographs and polysemes (English cry, French voler) must be resolved before translation (French pleurer or crier, English fly or steal); ambiguities of grammatical category (English light as noun, adjective or verb, face as noun or verb) must likewise be resolved for choice between lumière, clair or allumer, etc. He states that the examples of monolingual structural ambiguities occur when a word or phrase can potentially modify more than one element of a sentence. And he explains this through the following example, old men and women, the adjective old may refer only to men or to both men and women [15].

Prepositional phrases can modify almost any preceding verb or noun phrase,

e.g. (a) The car was driven by the teacher with great skill.

(b) The car was driven by the teacher with defective tyres.

(c) The car was driven by the teacher with red hair.

Lexical and structural ambiguities may and often combine: He saw her shaking hands, where shaking can be either an adjectiveƒ  hands which were shaking or a verb component ƒ that she was shaking hands [15].

Bilingual lexical ambiguities occur primarily when the TL makes distinctions absent in the SL: E.g. English river can be rivière or fleuve (Fluss or Strom);

English eat can be German essen or fressen;

English wall can be French mur or paroi, German Wand, Mauer or Wall.

Hutchins implies that an example which can inllustrate this is the translation of wear from English to Japanese. Although there is a generic verb kiru it is normal to use the verb appropriate to the type of item worn: haoru (coat or jacket), haku (shoes or trousers), kaburu (hat), hameru (ring or gloves), shimeru (belt, tie or scarf), tsukeru (brooch or clip), kakeru (glasses or necklace), hayasu (moustache) [15].

Also in this article is pointed out that the bilingual structural differences cover both general facts, for instance, in English the adjectives generally precede nouns but that they usually follow them in French, and differences conditioned by specific lexical differences. A familiar example occurs when translating the English verb likeƒ  She likes to play tennis, as a German adverb gern ƒ Sie spielt gern Tennis [15].

Other examples are:

simple verbs (trust) rendered by circumlocutions (avoir confiance à);

single clauses ƒ He pushed open the door restructured as a subordinate clause ƒ Il a ouvert la porte en la poussant [14].

The structural differences combine with lexical differences, for instance the translation of know into French or German, where choice of connaître (kennen) or savoir (wissen) affects both structure ƒ Je connais l’homme. (Ich kenne den Mann); Je sais ce qu’il s’appelle. (Ich weiss wie er heisst) and the translation of other lexical items (what as ce que and wie) [14].

The morphological analysis is concerned with the identification of base forms from infected forms of nouns, verbs and adjectives (irregular forms being entered as units in dictionaries), with the recognition of derivational forms (e.g. English -ly as an adverb derived from an adjective, German -heit as a noun from an adjective), and with the segmentation of compound forms in languages like German (Dampfschiff, Dampfhammer) [14].

In the “An Introduction to Machine Translation” Hutchins reveals that all MT systems have problems with ‘unknown’ words, especially with the neologisms and new combinations. He says that if derivational elements and components can be correctly identified then can be translated with the ‘international’ equivalences of many elements, for instance, French demi- and English semi-, French -ique and English -ic) [16].

However, segmentation can be problematic, e.g. extradition analysed as both extradit+ion and ex+tradition, cooperate as both co+operate and cooper+ate. He suggests that these would be resolved by dictionary consultation, but sometimes alternative segmentations are equally valid (German Wachtraum could be guard room (Wacht+Raum) or day dream (Wach+Traum), until one is eliminated at a later stage [16].

In his article “Machine Translation: A Brief History” Hutchins writes that in MT there are three basic approaches to syntactic structure analysis. The first aim is to identify legitimate sequences of grammatical categories, for instance, in English article + adjective + noun. This approach is based on predictive analysis, which is a sequence of categories predicted that the following

category would be one of a relatively limited set. The second aim to recognize groups of

categories, for instance, as noun phrases, verb phrases, clauses, and ultimately sentences. These are based on phrase structure or constituency grammar. The third aim to identify dependencies among categories, for example, reflecting the fact that prepositions determine the case forms of German and Russian nouns, that the form of a French adjective is determined by the noun it modifies. The basis is dependency grammar [14].

He also claims that SL structures are transformed into equivalent TL structures by conversion rules, in the case of phrase structure or dependency trees by ‘tree transducers’, which may apply either unconditionally, for example, English adjective+noun to French noun+adjective or conditionally, followed by specific lexical items, for example, English like to German gern [15].

Another problem which identifies Arnold is the multiword units like idioms and collocations. The real problem with idioms is that they are not generally fixed in their form [3].

Hutchins in his article “Machine Translation: History and General principles” points out that MT systems can fail for many practical reasons, for instance, unknown words ƒ neologisms or new compounds, misspellings ƒ supercede, persue, British orthography instead of expected American ƒ traveller for traveler, typographical errors ƒ from instead of form, wrong usages ƒ principle as an adjective, ungrammaticalness ƒ none of them were present. Even if full disambiguation cannot be achieved, a crude translation may be obtained with basic phrase structure identification. It is now common for systems to retain information from all levels of analysis; thus transfer (or interlingual) representations will combine morphological, syntactic, semantic and thematic information [15].

Historically, MT systems have progressively introduced ‘deeper’ levels of analysis and

transfer. Early word-for-word systems were restricted to bilingual dictionaries and simple

morphology. Later ‘direct’ systems introduced syntactic analysis and synthesis. Phrase structure and dependency analyses provided the basis for simple transfer systems with little semantic analysis.

Conclusion

The use of machine translation is more important than we may think. It could be claimed that the resources available to the translator through information technology imply a change in the relationship between the translator and the text, that is to say, a new way of translating. However, there is the development of new capabilities, which leads us to point out a number of essential aspects of the current situation. Translating with the help of the computer is definitely not the same as working exclusively on paper and with paper products such as conventional dictionaries, because computer tools provide us with a relationship to the text which is much more flexible than a purely lineal reading. Furthermore, the Internet with its universal access to information and instant communication between users has created a physical and geographical freedom for translators that were inconceivable in the past. Translators need to accept the new technologies and learn how to use them to their maximum potential as a means to increased productivity and quality improvement. As we mentioned there are problems of ambiguity when working with MT, and those problems are also common for us. A clear example would be translations from Spanish to Basque. In those translations, apart from ambiguity problems, there would be structural problems, because structurally Spanish and Basque are completely different.

Having analyzed some theoretical sources we came to the following conclusions:

Machine translations enable people to have information in many languages, helping to understand it without knowing the language;

MT provides translators useful tools that help them to make their job more efficiently and faster;

It can output much larger volumes of translation than any team of translators;

Machine translation rarely reaches accuracy levels above 70%;

Machine translation is a venerable scientific enterprise, a technological challenge of the first order and it can be understood as an economic necessity;

Machine translation is an automatic linguistic translation”, namely, a word-by-word translation;

Machine translation refers to the utilization of software to translate text from one language to another language;

In the process of translation Machine Translations encounter some problems of ambiguity that make that their use to be hard.

This research could be a good basis for a further development of this topic, namely, a profound analysis of different machine translation and their accuracy in translating. We consider that the given study might be of great use to researchers in the field of translation and linguistics. It may serve as a reference point for the elaboration of year and graduation papers.

Finally, we should point out that machine translation has an important role in the process of translation and is very helpful for translators.

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