Machine translation

Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another.

On a basic level, MT performs simple substitution of words in one natural language for words in another, but that alone usually cannot produce a good translation of a text, because recognition of whole phrases and their closest counterparts in the target language is needed. Solving this problem with corpus and statistical techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies.^[1]

Current machine translation software often allows for customization by domain or profession (such as weather reports), improving output by limiting the scope of allowable substitutions. This technique is particularly effective in domains where formal or formulaic language is used. It follows that machine translation of government and legal documents more readily produces usable output than conversation or less standardised text.

Improved output quality can also be achieved by human intervention: for example, some systems are able to translate more accurately if the user has unambiguously identified which words in the text are names. With the assistance of these techniques, MT has proven useful as a tool to assist human translators and, in a very limited number of cases, can even produce output that can be used as is (e.g., weather reports).

The progress and potential of machine translation has been debated much through its history. Since the 1950s, a number of scholars have questioned the possibility of achieving fully automatic machine translation of high quality.^[2] Some critics claim that there are in-principle obstacles to automatizing the translation process.^[3]

History

The idea of machine translation may be traced back to the 17th century. In 1629, René Descartes proposed a universal language, with equivalent ideas in different tongues sharing one symbol. The field of “machine translation” appeared in Warren Weaver’s Memorandum on Translation (1949). The first researcher in the field, Yehosha Bar-Hillel, began his research at MIT (1951). A Georgetown MT research team followed (1951) with a public demonstration of its system in 1954. MT research programs popped up in Japan and Russia (1955), and the first MT conference was held in London (1956). Researchers continued to join the field as the Association for Machine Translation and Computational Linguistics was formed in the U.S. (1962) and the National Academy of Sciences formed the Automatic Language Processing Advisory Committee (ALPAC) to study MT (1964). Real progress was much slower, however, and after the ALPAC report (1966), which found that the ten-year-long research had failed to fulfill expectations, funding was greatly reduced.

The French Textile Institute also used MT to translate abstracts from and into French, English, German and Spanish (1970); Brigham Young University started a project to translate Mormon texts by automated translation (1971); and Xerox used SYSTRAN to translate technical manuals (1978). Beginning in the late 1980s, as computational power increased and became less expensive, more interest was shown in statistical models for machine translation.Various MT companies were launched, including Trados (1984), which was the first to develop and market translation memory technology (1989). The first commercial MT system for Russian/ English / German-Ukrainian was developed at Kharkov State University (1991).

MT on the web started with SYSTRAN Offering free translation of small texts (1996), followed by AltaVisa Babelfish, which racked up 500,000 requests a day (1997). Franz-Josef Och (the future head of Translation Development AT Google) won DARPA’s speed MT competition (2003). More innovations during this time included MOSES, the open-source statistical MT engine (2007), a text/ SMS translation service for mobiles in Japan (2008), and a mobile phone with built-in-speech-to-speech translation functionality for English, Japanese and Chinese (2009). Recently, Google announced that Google Translate translates roughly enough text to fill 1 million books in one day (2012).

The idea of using digital computers for translation of natural languages was proposed as early as 1946 by A. D. Booth and possibly others. Warren Weaver wrote an important memorandum "Translation" in 1949. The Georgetown experiment was by no means the first such application, and a demonstration was made in 1954 on the APEXC machine at Birkbeck College (University of London) of a rudimentary translation of English into French. Several papers on the topic were published at the time, and even articles in popular journals (see for example Wireless World, Sept. 1955, Cleave and Zacharov). A similar application, also pioneered at Birkbeck College at the time, was reading and composing Braille texts by computer.

Translation process

The human translation process may be described as:

1. Decoding the meaning of the source text; and
2. Re-encoding this meaning in the target language.

Behind this ostensibly simple procedure lies a complex cognitive operation. To decode the meaning of the source text in its entirety, the translator must interpret and analyse all the features of the text, a process that requires in-depth knowledge of the grammar, semantics, syntax, idioms, etc., of the source language, as well as the culture of its speakers. The translator needs the same in-depth knowledge to re-encode the meaning in the target language.

Therein lies the challenge in machine translation: how to program a computer that will "understand" a text as a person does, and that will "create" a new text in the target language that "sounds" as if it has been written by a person.

This problem may be approached in a number of ways.

Approaches

Bernard Vauquois' pyramid showing comparative depths of intermediary representation, interlingual machine translation at the peak, followed by transfer-based, then direct translation.

Machine translation can use a method based on linguistic rules, which means that words will be translated in a linguistic way — the most suitable (orally speaking) words of the target language will replace the ones in the source language.

It is often argued that the success of machine translation requires the problem of natural language understanding to be solved first.

Generally, rule-based methods parse a text, usually creating an intermediary, symbolic representation, from which the text in the target language is generated. According to the nature of the intermediary representation, an approach is described as interlingual machine translation or transfer-based machine translation. These methods require extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules.

Given enough data, machine translation programs often work well enough for a native speaker of one language to get the approximate meaning of what is written by the other native speaker. The difficulty is getting enough data of the right kind to support the particular method. For example, the large multilingual corpus of data needed for statistical methods to work is not necessary for the grammar-based methods. But then, the grammar methods need a skilled linguist to carefully design the grammar that they use.

To translate between closely related languages, a technique referred to as Transfer-based machine translation may be used.

Rule-based

The rule-based machine translation paradigm includes transfer-based machine translation, interlingual machine translation and dictionary-based machine translation paradigms.

Transfer-based machine translation

Interlingual

Interlingual machine translation is one instance of rule-based machine-translation approaches. In this approach, the source language, i.e. the text to be translated, is transformed into an interlingual, i.e. source-/target-language-independent representation. The target language is then generated out of the interlingua.

Dictionary-based

Machine translation can use a method based on dictionary entries, which means that the words will be translated as they are by a dictionary.

Statistical

Statistical machine translation tries to generate translations using statistical methods based on bilingual text corpora, such as the Canadian Hansard corpus, the English-French record of the Canadian parliament and EUROPARL, the record of the European Parliament. Where such corpora are available, good results can be achieved translating similar texts, but such corpora are still rare for many language pairs. The first statistical machine translation software was CANDIDE from IBM. Google used SYSTRAN for several years, but switched to a statistical translation method in October 2007.^{[citation needed][4]} In 2005, Google improved its internal translation capabilities by using approximately 200 billion words from United Nations materials to train their system; translation accuracy improved.^[5]

Example-based

Example-based machine translation (EBMT) approach was proposed by Makoto Nagao in 1984.^[6][7] It is often characterised by its use of a bilingual corpus as its main knowledge base, at run-time. It is essentially a translation by analogy and can be viewed as an implementation of case-based reasoning approach of machine learning.

Hybrid MT

Hybrid machine translation (HMT) leverages the strengths of statistical and rule-based translation methodologies.^[8] Several MT organizations (such as Asia Online, LinguaSys, Systran, and Polytechnic University of Valencia) claim a hybrid approach that uses both rules and statistics. The approaches differ in a number of ways:

• Rules post-processed by statistics: Translations are performed using a rules based engine. Statistics are then used in an attempt to adjust/correct the output from the rules engine.
• Statistics guided by rules: Rules are used to pre-process data in an attempt to better guide the statistical engine. Rules are also used to post-process the statistical output to perform functions such as normalization. This approach has a lot more power, flexibility and control when translating.

Major issues

Disambiguation

Word-sense disambiguation concerns finding a suitable translation when a word can have more than one meaning. The problem was first raised in the 1950s by Yehoshua Bar-Hillel.^[9] He pointed out that without a "universal encyclopedia", a machine would never be able to distinguish between the two meanings of a word.^[10] Today there are numerous approaches designed to overcome this problem. They can be approximately divided into "shallow" approaches and "deep" approaches.

Shallow approaches assume no knowledge of the text. They simply apply statistical methods to the words surrounding the ambiguous word. Deep approaches presume a comprehensive knowledge of the word. So far, shallow approaches have been more successful.^{[citation needed]}

The late Claude Piron, a long-time translator for the United Nations and the World Health Organization, wrote that machine translation, at its best, automates the easier part of a translator's job; the harder and more time-consuming part usually involves doing extensive research to resolve ambiguities in the source text, which the grammatical and lexical exigencies of the target language require to be resolved:

Why does a translator need a whole workday to translate five pages, and not an hour or two? ..... About 90% of an average text corresponds to these simple conditions. But unfortunately, there's the other 10%. It's that part that requires six [more] hours of work. There are ambiguities one has to resolve. For instance, the author of the source text, an Australian physician, cited the example of an epidemic which was declared during World War II in a "Japanese prisoner of war camp". Was he talking about an American camp with Japanese prisoners or a Japanese camp with American prisoners? The English has two senses. It's necessary therefore to do research, maybe to the extent of a phone call to Australia.^[11]

The ideal deep approach would require the translation software to do all the research necessary for this kind of disambiguation on its own; but this would require a higher degree of AI than has yet been attained. A shallow approach which simply guessed at the sense of the ambiguous English phrase that Piron mentions (based, perhaps, on which kind of prisoner-of-war camp is more often mentioned in a given corpus) would have a reasonable chance of guessing wrong fairly often. A shallow approach that involves "ask the user about each ambiguity" would, by Piron's estimate, only automate about 25% of a professional translator's job, leaving the harder 75% still to be done by a human.

Named entities

Related to named entity recognition in information extraction. Name entities, in narrow sense, refer to concrete and abstract entities in the real world including people, organizations, companies, places etc. In also refers to expressing of time, space, quantity such as July 1st, 2011, $79.99 and so on.^[12] Name entity is part of the basic information of the text and the basis for good understanding. In MT, it’s essential for the systems to distinguish these named entities as it will affect the quality of named-entity output. Name entities include person names, place names, organization names, etc. And there is no unified standard for the machine translators to translate the name entities. Thus, it is a big problem for MT output.

Ontologies in MT

An ontology is a formal representation of knowledge which includes the concepts (such as objects, processes etc.) in a domain and some relations between them. If the stored information is of linguistic nature, one can speak of a lexicon.^[13] In NLP, ontologies can be used as a source of knowledge for machine translation systems. With access to a large knowledge base, systems can be enabled to resolve many (especially lexical) ambiguities on their own. In the following classic examples, as humans, we are able to interpret the prepositional phrase according to the context because we use our world knowledge, stored in our lexicons:

"I saw a man/star/molecule with a microscope/telescope/binoculars."^[13]

A machine translation system initially would not be able to differentiate between the meanings because syntax does not change. With a large enough ontology as a source of knowledge however, the possible interpretations of ambiguous words in a specific context can be reduced. Other areas of usage for ontologies within NLP include information retrieval, information extraction and text summarization.^[13]

Building ontologies

The ontology generated for the PANGLOSS knowledge-based machine translation system in 1993 may serve as an example of how an ontology for NLP purposes can be compiled:^[14]

• A large-scale ontology is necessary to help parsing in the active modules of the machine translation system.
• In the PANGLOSS example, about 50.000 nodes were intended to be subsumed under the smaller, manually-built upper (abstract) region of the ontology. Because of its size, it had to be created automatically.
• The goal was to merge the two resources LDOCE online and WordNet to combine the benefits of both: concise definitions from Longman, and semantic relations allowing for semi-automatic taxonomization to the ontology from WordNet.
  • A definition match algorithm was created to automatically merge the correct meanings of ambiguous words between the two online resources, based on the words that the definitions of those meanings have in common in LDOCE and WordNet. Using a similarity matrix, the algorithm delivered matches between meanings including a confidence factor. This algorithm alone, however, did not match all meanings correctly on its own.
  • A second hierarchy match algorithm was therefore created which uses the taxonomic hierarchies found in WordNet (deep hierarchies) and partially in LDOCE (flat hierarchies). This works by first matching unambiguous meanings, then limiting the search space to only the respective ancestors and descendants of those matched meanings. Thus, the algorithm matched locally unambiguous meanings (for instance, while the word seal as such is ambiguous, there is only one meaning of "seal" in the animal subhierarchy).
• Both algorithms complemented each other and helped constructing a large-scale ontology for the machine translation system. The WordNet hierarchies, coupled with the matching definitions of LDOCE, were subordinated to the ontology's upper region. As a result, the PANGLOSS MT system was able to make use of this knowledge base, mainly in its generation element.

Applications

While no system provides the holy grail of fully automatic high-quality machine translation of unrestricted text, many fully automated systems produce reasonable output.^[15][16][17] The quality of machine translation is substantially improved if the domain is restricted and controlled.^[18]

Despite their inherent limitations, MT programs are used around the world. Probably the largest institutional user is the European Commission. The MOLTO project, for example, coordinated by the University of Gothenburg, received more than 2.375 million euros project support from the EU to create a reliable translation tool that covers a majority of the EU languages.^[19]

Google has claimed that promising results were obtained using a proprietary statistical machine translation engine.^[20] The statistical translation engine used in the Google language tools for Arabic <-> English and Chinese <-> English had an overall score of 0.4281 over the runner-up IBM's BLEU-4 score of 0.3954 (Summer 2006) in tests conducted by the National Institute for Standards and Technology.^[21][22][23]

With the recent focus on terrorism, the military sources in the United States have been investing significant amounts of money in natural language engineering. In-Q-Tel^[24] (a venture capital fund, largely funded by the US Intelligence Community, to stimulate new technologies through private sector entrepreneurs) brought up companies like Language Weaver. Currently the military community is interested in translation and processing of languages like Arabic, Pashto, and Dari.^{[citation needed]} The Information Processing Technology Office in DARPA hosts programs like TIDES and Babylon Translator. US Air Force has awarded a $1 million contract to develop a language translation technology.^[25]

The notable rise of social networking on the web in recent years has created yet another niche for the application of machine translation software – in utilities such as Facebook, or instant messaging clients such as Skype, GoogleTalk, MSN Messenger, etc. – allowing users speaking different languages to communicate with each other. Machine translation applications have also been released for most mobile devices, including mobile telephones, pocket PCs, PDAs, etc. Due to their portability, such instruments have come to be designated as mobile translation tools enabling mobile business networking between partners speaking different languages, or facilitating both foreign language learning and unaccompanied traveling to foreign countries without the need of the intermediation of a human translator.

Evaluation

Machine translation systems and output can be evaluated along numerous dimensions. The intended use of the translation, characteristics of the MT software, the nature of the translation process, etc., all affect how one evaluates MT systems and their output. The FEMTI taxonomy of dimensions, with associated evaluation metrics, appears at http://www.issco.unige.ch:8080/cocoon /femti/st-home.html .

There are various means for evaluating the output quality of machine translation systems. The oldest is the use of human judges^[26] to assess a translation's quality. Even though human evaluation is time-consuming, it is still the most reliable way to compare different systems such as rule-based and statistical systems. Automated means of evaluation include BLEU, NIST and METEOR.

Relying exclusively on unedited machine translation ignores the fact that communication in human language is context-embedded and that it takes a person to comprehend the context of the original text with a reasonable degree of probability. It is certainly true that even purely human-generated translations are prone to error. Therefore, to ensure that a machine-generated translation will be useful to a human being and that publishable-quality translation is achieved, such translations must be reviewed and edited by a human.^[27] The late Claude Piron wrote that machine translation, at its best, automates the easier part of a translator's job; the harder and more time-consuming part usually involves doing extensive research to resolve ambiguities in the source text, which the grammatical and lexical exigencies of the target language require to be resolved.^[11] Such research is a necessary prelude to the pre-editing necessary in order to provide input for machine-translation software such that the output will not be meaningless.^[28]

In certain applications, however, e.g., product descriptions written in a controlled language, a dictionary-based machine-translation system has produced satisfactory translations that require no human intervention save for quality inspection.^[29]

Through comparing the output of three different type of articles (i.e. poem, novel and exposition) by Google Translate and Youdao Translate, the author concludes some advantages and disadvantages of machine translation.

When translating poems, it is necessary to pay attention to rhythm and connotation according with the atmosphere. Machine translation often translates word by word but neglects the rhythm, making it seems not like a poem. When translating novels, some verbs and the logic of the story are important. One disadvantage of machine translation is the mechanical way of translation, which may make a story lack of logic and difficult to understand, such as the translation of the conjunction word ‘and’. When it comes to expositions, attention should be paid on precise choices of lexical translation. Because it is a type of article whose expression is relatively objective and clear. Machine translation may sometimes chooses improper translation that are not fit for this kind of context when facing polysemes and grammatical problems can also be found here.

Despite of those disadvantages above, machine translation still maintain some advantages. First, machine translation is much faster than human translation. Second, machine translation has a much huger quantity of vocabulary than human. Although post-editing is still needed by translators, they only need to adjust some words or grammar according to the ready-made target texts from machine translation. This will greatly improve the speed and efficiency of translators. As a result, it is undoubtedly that human translation should integrate with machine translation to make up for each other’s deficiencies. The author also hopes that with the further research and development of machine translation, it can be capable of translating articles according to different types in the near future.

Copyright

Only works that are original are subject to copyright protection, so some scholars claim that machine translation results are not entitled to copyright protection because MT does not involve creativity.^[30] The copyright at issue is for a derivative work; the author of the original work in the original language does not lose his rights when a work is translated: a translator must have permission to publish a translation.

See also

• Perbandingan -- machine translation applications
• Statistical machine translation
• Artificial Intelligence
• Cache language model
• Computational linguistics
• Universal Networking Language
• Computer-assisted translation and Translation memory
• Foreign language writing aid
• Controlled natural language
• Fuzzy matching
• Postediting
• History of machine translation
• Human Language Technology
• Language barrier
• Daftar/Tabel -- emerging technologies
• Daftar/Tabel -- research laboratories for machine translation
• Pseudo-translation
• Translation
• Translation memory
• Universal translator
• Phraselator
• Mobile translation

Notes

References

• Cohen, J. M. (1986), "Translation", Encyclopedia Americana, 27, pp. 12–15
• Hutchins, W. John; Somers, Harold L. (1992). An Introduction to Machine Translation. London: Academic Press. ISBN 0-12-362830-X. 
• Piron, Claude (1994) (in French), Le défi des langues — Du gâchis au bon sens [The Language Challenge: From Chaos to Common Sense], Paris: L'Harmattan, ISBN 9782738424327, http://books.google.com/books/about/L e_d%C3%A9fi_des_langues.html?id=w518G Ei29qkC

External links

• Statistical Machine Translation
• International Association for Machine Translation (IAMT)
• Machine Translation Archive by John Hutchins. An electronic repository (and bibliography) of articles, books and papers in the field of machine translation and computer-based translation technology
• Machine translation (computer-based translation) — Publications by John Hutchins (includes PDFs of several books on machine translation)
• Machine Translation and Minority Languages
• John Hutchins 1999