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Paidalangan sozdikter, resmi kuzhattar, okulyktar, audarylgan enbekter:
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Introduction
Machine translate- the process of translating texts (written, and ideally oral) from one natural language to another using a special computer program. Also called direction. scientific research associated with the construction of such systems.
Instead of "machine" the word automatic is sometimes used, which does not affect the meaning. However, do not confuse machine translation with automated one, it has a completely different meaning - with it, the program simply helps a person translate texts.
The idea to use electronic computers (computers) for translation was expressed in 1947 in the United States, immediately after the appearance of the first computers. The first public demonstration of machine translation took place in 1954. Despite the primitiveness of that system, this experiment received a wide response.
By the mid-1960s, two systems of Russian-English translation were provided for practical use in the United States:
- MARK
- GAT
However, the ALPAC commission created to evaluate such systems came to the conclusion that, due to the low quality of machine translated texts, this activity is unprofitable in the US. While the commission recommended continuing and deepening theoretical developments, in general, her conclusions led to an increase in pessimism, a decrease in funding, and often to a complete cessation of work on this topic.
Nevertheless, research continued in a number of countries, helped by the constant advances in computing technology. A particularly significant factor was the emergence of mini- and personal computers, and with them increasingly complex vocabulary, search engines, focused on working with natural language data. The need for translation as such also grew due to the growth of international relations. All this led to a new rise in this area. The time has come for the widespread practical use of translation systems, and a market for commercial developments on this topic has emerged.
However, high-quality translation of texts on a wide range of topics is still unattainable. However, the acceleration of the translator's work when using machine translation systems is undoubted.
1. Main body
Machine translation systems fall into three categories:
- -systems based on grammar rules(Rule-Based Machine Translation, RBMT),
- -statistical systems(Statistical Machine Translation, SMT)
- -hybrid systems combining the advantages of both (are the most promising)
Rule-based machine translation- a general term that refers to machine translation systems based on linguistic information about the source and target languages. They consist of bilingual dictionaries and grammars, covering the main semantic, morphological, syntactic patterns of each language. This approach to machine translation is also called classic. Based on these data, the source text is sequentially, by sentences, converted into the translated text. The principle of operation of such systems is the connection between the structure of the input and output sentences. computer machine translation
RBMT systems are divided into three groups:
- word-for-word translation systems;
- Transfer systems (Transfer) - transform the structures of the input language into grammatical structures of the output language;
- Interlinguistic systems (Interlingua) - an intermediate language for describing meaning.
The main advantage of transfer-based systems is the high completeness of text coverage with an acceptable level of translation quality, as well as low costs for primary development and modernization.
Components of a typical RBMT:
- · Linguistic databases: - bilingual dictionaries; - files of names, transliteration; - morphological tables.
- · Translation module: - grammar rules; - translation algorithms.
- · Advantages of RBMT systems:
- - syntactic and morphological accuracy;
- - stability and predictability of the result;
- - the ability to customize the subject area.
- · Disadvantages of RBMT systems:
- - laboriousness and duration of development;
- - the need to maintain and update linguistic databases;
- - "machine accent" when translating.
Statistical machine translation- a kind of machine translation, where the translation is generated on the basis of statistical models, the parameters of which are derived from the analysis of bilingual text corpora (text corpora).
Statistical machine translation is contrasted with rule-based machine translation (RBMT) and Example-Based MT (EBMT) systems.
The first ideas for statistical machine translation were published by Warren Weaver in 1949. "Second wave" - early 1990s, IBM. "Third Wave" - Google, Microsoft, Language Weaver, Yandex.
Statistical translation models:
- according to words (Word-based translation - WBT)
- by phrases (Phrase-based translation - PBT)
- syntax (Syntax-based translation - SBT)
- Hierarchical phrase-based translation - HPBT
Advantages of SMT:
- ・Quick setup
- · Easily add new translation directions
- Smoothness of translation
Disadvantages of SMT:
- "Deficiency" of parallel cases
- Numerous grammatical errors
- Translation instability
To improve the quality, developers of machine translation systems introduce some "cross-cutting" rules, thereby turning purely statistical systems into Hybrid machine translation. The addition of some rules, that is, the creation of hybrid systems, somewhat improves the quality of translations, especially when the amount of input data used to build the machine translator index is insufficient.
Hybrid machine translation- integration of different machine translation approaches from options MP:
- · Rule-based machine translation (RBMT) - Rule-based machine translation.
- · Corpus-based machine translation (CBMT) - Machine translation of text corpora.
- · Example-based machine translation (EBMT)
- · Statistical machine translation (SMT) - Statistical machine translation.
The hybrid architecture is expected to combine the benefits of these approaches.
Hybrid translation technology involves the use statistical methods to build vocabulary databases automatically based on parallel corpora, generate several possible translations, both at the lexical level and at the level of the syntactic structure of the output language sentence, apply post-editing in automatic mode and select the best (most probable) possible translation based on the language model , built according to a certain corpus of the target language.
Statistical MT seeks to use linguistic data, while systems with a "classic" rule-based approach apply statistical methods. The addition of some "end-to-end" rules, that is, the creation of hybrid systems, somewhat improves the quality of translations, especially in the case of insufficient input data used in the construction of index files for storing linguistic information of a machine translator based on N-grams.
Hybrid technology architecture "SMT and RBMT"[
The RBMT system is supplemented with two components: a statistical post-editing module and a language model module. Statistical post-editing allows you to smoothen the RB translation, bringing it closer to natural language while maintaining a clear structure of the synthesized text. Language models are used to evaluate the smoothness and grammatical correctness of the translations generated by the hybrid system.
Typical HMT Architecture:
- · Parallel case;
- · Education;
- · Language model;
- · Data for post-editing;
- · Rules of synthesis;
- · Glossary of terminology.
Benefits of hybrid translation:
- · Fast automatic adjustment based on the customer's Translation Memories;
- · Terminological accuracy of the translation, as well as the unity of style;
- · Obtaining additional useful data - a bilingual terminological dictionary.
Conclusion
The main advantage of machine translation is that it allows you to quickly deal with very large volumes of text and therefore sometimes turns out to be more cost-effective than manual translation. At the same time, it should be remembered that the quality of machine translation will always be inferior to human translation. Therefore, it is advisable to use it only in certain cases.
Many types of materials are in principle not intended for machine translation. Thus, texts cannot be trusted to the machine, where the inaccuracy of the translation can endanger human health, the performance of a complex device or a large contract - the time saved here does not justify the risk. Any documents that imply legal responsibility require human control. Machine translation is unsuitable for marketing materials, where the text is actually rethought in a new cultural context and created anew.
Acceptable quality can be expected when translating strictly formalized technical texts, while literary and advertising texts are not amenable to machine translation.
When resorting to machine translation, it is important not only to have a clear idea of the desired result and understand the limitations of this method, but also to take into account one more factor. MT systems usually require complex individual tuning and refinement, including "training" on a specific topic - without this, they show much worse results. In this regard, it makes sense to use machine translation only if you have to translate huge volumes of the same type of texts. In this case, it will be economically feasible to spend some time training the system, then apply machine translation and get the text suitable for post-editing as output. If we are talking about several dozen pages, trying to implement machine translation is pointless and simply unprofitable.
Thus, machine translation with post-editing can be really beneficial if texts of the right type are translated in very large volumes. Because the large volumes translations pass through translation companies that often specialize in specific subject areas, the introduction of fairly effective, but expensive, latest-generation machine translation systems is economically justified in such companies: neither content providers, even large ones, nor individual translators will be able to effectively use machine translation on their own .
Used Books
- 1. http://www.logrus.ru
- 2. http://www.moluch.ru/
- 3.https://www.academia.edu
- 4. http://study-english.info/
Machine translation, or rather, computer translation, is also a written translation, because as a result we get a written text. However, it is not carried out by a translator, but by a special computer program. Modern computer translation programs are quite advanced, but they still cannot solve the most difficult task of the translation process: the choice of a contextually necessary option, which in each text is due to many reasons. Currently, the result of this type of translation can be used as a draft version of the future text, which will be edited by the translator, as well as a means to get a general idea of the topic and content of the text even in the extreme situation of the absence of a translator.
An even more difficult task is the translation of oral text using computer programs, since the problem of oral speech recognition is only at the initial stage of its solution. Until now, an insurmountable obstacle is the individual coloring of the sound of a segment of speech - in any language, such speech is poorly formalized.
Syntactic structure pre-editing can be:
· splitting an extra-long sentence (more than 40 words) into several shorter ones, while adding (if necessary) linking elements;
· introduction to English text articles where necessary or grammatically justified;
· repetition of elements in the coordinative connection of phrases in a sentence;
· the introduction of unions when using an allied connection between sentences;
· elimination of structures in brackets in the middle of a noun phrase or in the middle of a sentence;
· replacing occasional abbreviations with full names or introducing special characters that do not allow their translation;
· elimination of lexical and logical ellipses, informal constructions and metaphors;
· bringing to a single form constructions or compound words that can be found in the text in continuous, hyphenated and free spelling.
The manually edited text is then automatically processed in the MP system.
25. General scheme of machine translation.
All over the world, the use of machine translation systems, despite all their weaknesses, has long been an element of the professional work of a translator who must be able to use a computer not only as a typewriter. The concept of a translator's workstation, which includes a complex of resident dictionaries, thesauri, spell-checking systems, systems for accessing information over various data transmission networks, should become commonplace for a specialist philologist.
A machine translation (MT) system of texts can be used as part of such a translator's workstation, while providing a high-quality translation that is strictly focused on a specific subject area, user tasks and type of documentation. In addition, such a system can help a user who does not know foreign language, very quickly and at low cost to get an approximate (rough) translation of texts in the field of knowledge of interest to him, a translation sufficient to understand the information transmitted by the text in a foreign language.
General requirements for practical systems
machine translation (MT)
· System stability. The MT system should give a result that can be used even in the case of defects in the source material and incomplete vocabulary.
· System replicability. The system should have fairly simple software and linguistic tools to expand the scope of its application.
· System adaptability. The MP system should have the means of customization to the needs of specific users and the features of the processed documents.
· Timing Optimality. The speed of translation of texts must correspond either to the volume of information received per unit of time, or to the norms of users' work.
· User comfort. Service tools of the system should ensure the convenience of the user in all possible modes of operation in the system.
When working with a particular machine translation system, it should be remembered that translation is carried out at several subordinate levels of the system implementation.
These levels generally include:
· level of automatic text pre-editing;
· level of lexical and morphological analysis;
· the level of contextual analysis and group analysis;
· level of analysis of functional segments;
· level of proposal analysis;
· the level of output text synthesis;
· automatic post-editing level.
Approaches to machine translation
Machine translation systems can use a translation method based on linguistic rules. The most suitable words from the source language are simply replaced with words from the target language.
It is often argued that in order to successfully solve the problem of machine translation, it is necessary to solve the problem of understanding text in natural language.
As a rule, the rule-based translation method uses a symbolic representation (intermediary), on the basis of which the text in the target language is created. And if we take into account the nature of the intermediary, then we can talk about interlinguistic machine translation or transfer machine translation. These methods require very large dictionaries with morphological, syntactic and semantic information and a large set of rules.
If the machine translation system has enough data, a good quality translation can be obtained. The main difficulty lies in the formation of these data. For example, large text corpora required for statistical methods of translation are not sufficient for grammar-based translation. Moreover, for the latter, an additional task of grammar is required.
For the translation of related languages (Russian, Ukrainian), a simple replacement of words may be sufficient.
Modern machine translation systems are divided into three large groups:
rule-based
based on examples
SMP rules based
Rule-based machine translation systems is a general term that refers to machine translation systems based on linguistic information about the source and target languages.
They consist of bilingual dictionaries and grammars, covering the main semantic, morphological, syntactic patterns of each language. This approach to machine translation is also called classical.
Based on these data, the source text is sequentially, by sentences, converted into the translated text. Often, such systems are contrasted with machine translation systems that are based on examples.
The principle of operation of such systems is the connection between the structure of the input and output sentences. The translation is not of particularly good quality. But on simple examples works.
Translation from English to German will look like this:
A girl eats an apple. Ein Madchen isst einen Apfel.
These systems fall into three groups:
word-for-word translation systems;
· transfer systems;
interlinguistic;
Word translation
Such systems are now used extremely rarely due to the poor quality of translation. The words of the source text are converted (as is) into the words of the translated text. Often such a transformation occurs without lemmatization and morphological analysis. This is the simplest machine translation method. It is used to translate long lists of words (eg directories). It can also be used to compose interlinear for TM-systems.
Transfer systems
Like transfer systems, and interlinguistic, have the same general idea. For translation, it is necessary to have an intermediary that carries the meaning of the translated expression. In interlinguistic systems, the mediator does not depend on a pair of languages, while in transfer systems it does.
Transfer systems work on a very simple principle: rules are applied to the input text that match the structures of the source and target languages. The initial stage of work includes morphological, syntactic (and sometimes semantic) analysis of the text to create an internal representation. The translation is generated from this representation using bilingual dictionaries and grammar rules. Sometimes, based on the primary representation, which was obtained from the source text, a more "abstract" internal representation is built. This is done in order to emphasize places important for translation, and to discard non-essential parts of the text. When constructing the translation text, the transformation of the levels of internal representations occurs in the reverse order.
When using this strategy, it is enough high quality translations, with an accuracy in the region of 90% (although this is highly dependent on the language pair). The operation of any transfer system consists of at least five parts:
lexical transfer;
structural transfer;
morphological generation.
Morphological analysis. The words of the source text are classified by parts of speech. Their morphological features are revealed. Word lemmas are defined.
Lexical categorizations. In any text, some words may have more than one meaning, causing ambiguity in the analysis. Lexical categorization reveals the context of a word. Various notes and clarifications are possible.
Lexical transfer. On the basis of a bilingual dictionary, the lemmas of words are translated. The action is very similar to word-for-word translation.
structural transfer. The words agree in the sentence.
Morphological generation. Based on the output data of the structural transfer, word forms of the translated text are created.
One of the main features of transcendent machine translation systems is the stage during which the intermediate representation of the text in the source language is "transferred" to the intermediate representation of the text in the target language. This can work on one of the two levels of linguistic analysis, or both.
1. Surface (syntactic) transfer. This level is characterized by the transfer of "syntactic structures" between the source and target languages. Suitable for languages in the same family or the same type, such as in Romance languages, between Italian Spanish, Catalan, French, etc.
2. Deep (semantic) transfer. The level is characterized by a semantic representation. It depends on the original language. This representation may consist of a number of structures that represent a value. Translation also usually requires a structural transfer. This level is used for translation between more distant languages.
Interlinguistic machine translation
Interlinguistic machine translation is one of the classic approaches to machine translation. original the text is transformed into an abstract representation that is independent of the language (unlike transfer translation). The translated text is generated based on this representation. The main advantage of this approach is that to add a new language to the system. It can be proved mathematically that within the framework of this approach, the creation of each new language interpreter for such a system will reduce its cost, compared, for example, with a transfer translation system. Moreover, this approach can
· to implement "retelling of the text", paraphrasing of the source text within one language;
· a relatively simple implementation of the translation of very different languages, such as, for example, Russian and Arabic.
However, there are still no implementations of this approach that would work correctly for at least two languages. Many experts express doubts about the possibility of such an implementation. The biggest difficulty in creating such systems lies in the design of the cross-language representation. It must be both abstract and independent of specific languages, but at the same time it should reflect the features of any existing language. On the other hand, within the framework of artificial intelligence, the task of highlighting the meaning of the text on this moment still not resolved.
The interlinguistic approach was first proposed in the 17th century by Descartes and Leibniz, who proposed universal dictionaries using numerical codes. Others such as Cave Beck, Athanasius Kircher and Johann Joachim Becher worked to develop an unambiguous universal language based on the principles of logic and iconography.
In 1668, John Wilkins, in his treatise An Essay on Genuine Symbolism and Philosophical Language, spoke of his Interlingua.
In the 18th and 19th centuries, many universal languages were developed, including Esperanto. It is known that the idea of a universal language for machine translation did not manifest itself in any way at the initial stages of the development of this technology. Instead, only pairs of languages were considered. However, during the 1950s and 60s, researchers in Cambridge led by Margaret Masterman, in Leningrad led by Nikolai Andreev and in Milan Silvio Ceccato began work in this area.
In the 1970s and 1980s, some progress was made in this area and a number of machine translation systems were built.
In this translation method, interlingual representation can be thought of as a way of describing the analysis of a text, in the original language. At the same time, the morphological, syntactic characteristics of the text are preserved in the representation. It is assumed that in this way it is possible to convey the "meaning" when creating a translated text.
In this case, two interlingual representations are sometimes used. One of them more reflects the characteristics of the source language. The other is the target language. The translation in this case is carried out in two stages.
In some cases, two or more representations of the same level are used (equally close to both languages), but differing in subject matter. This is necessary to improve the quality of translation of specific texts.
This approach is not new to linguistics. It is based on the idea of the proximity of languages. To improve the quality of translation, natural language used as a bridge between two other languages. For example, when translating from Ukrainian into English, Russian is sometimes used.
To use the interlinguistic machine translation system, you need:
Dictionaries for analysis and generation of texts;
description of grammars of languages;
knowledge base of concepts (to create an interlingual representation);
· Concept projection rules for languages and representations.
The most difficult moment when creating this type is the inability to build a base for broad areas of knowledge. And those databases that are created for a very specific topic have a high computational complexity.