1、1The Problems With Computer-Assisted TranslationAbstract: For how long? Yes, the output from any Machine Translation (MT) software is still laughable. MT software is still in infancy. Most of what MT software does is word-for-word translation followed by some grooming based on a set of rules. No sur
2、prise, the result is barely readable. Still, the output may not be good enough for public display, so the question turns into: will the future of human translation be. proofreading computer output? Key words: translation skills source text Computer-Assisted Machine Translation 1.Current Situation: C
3、omputers can help with the translation process in many ways. There is software available to give you the gist of a document, as well as translation tools used by professional translators to help reduce translation time. However, language is a highly complex subject which does not always follow logic
4、al or set rules. To achieve usable, quality translations, it is essential to involve a human translator, ideally living 2in, and a native of, the country of the target language. 2.The Evolution of Machine Translation Languages are perhaps the most elusive element of the human culture: no one knows t
5、he creator, but still they survived through the centuries in hybrid forms, dashing out the ambitions of those, like some structuralists, who wanted to confine them to a strict domain of logical rules and signs. It isnt that languages do not have rules. But those rules (or at least some of them) are
6、only learnt after years of human interaction. Such a dictionary that would allow the user to find every single occurrence of every word in any language would be no more than a chimera. But it was such a chimera that presided the first feasibility trial of a translation machine that was carried out i
7、n 1954, as a joint project between IBM and the University of Georgetown. The first versions of machine translation software were based on detailed bilingual dictionaries that offered a number of equivalent words in the target language for each word listed in the source language, as well as a series
8、of rules on word order. However, initial optimism soon disappeared. Researchers began to think that the semantic barriers were overwhelming and no longer saw a solution on the near horizon to the problem of 3machine translation. Research continued in Canada, France and Germany and two machine transl
9、ation systems came into being several years later: “Systran“, used by the European Union Commission and “Taum-mto“, created by the University of Montreal to translate weather forecasts from French to English. Important advances occurred during the 1980s. The administrative and commercial needs of mu
10、ltilingual communities stimulated the demand for translation, leading to the development in countries such as France, Germany, Canada and Japan of new machine translation systems such as “Logos“ (from German to French and vice-versa) and the internal system created by the Pan-American Health Organiz
11、ation (from Spanish to English and vice-versa), as well as a number of systems produced by Japanese computer companies. Research also revived in the 1980s because large-scale access to personal computers and word-processing software produced a market for less expensive machine translation systems. T
12、he beginning of the 1990s saw vital developments in machine translation with a radical change in strategy from a translation based on grammatical rules to that based on bodies of texts and examples. Language was no longer perceived as a static entity 4governed by fixed rules, but as a dynamic body t
13、hat changes according to use and users, evolving through time and adapting to social and cultural realities. But the so-far results are still far from acceptable, despite some progresses on the technical field. To this day, machine translation continues its progress. Large companies are beginning to
14、 use it and software sales to the general public are increasing as well. This situation has led to the creation of on-line machine translation services which offer quick (but rarely efficient) translations in the desired language, as well as multilingual dictionaries, encyclopedias and free terminol
15、ogy databases. 3.Machine Translation vs. Computer Aided Translation (CAT) Machine Translation is often wrongly mixed with Computer Aided Translation (CAT). These two technologies are the offspring of different approaches. They do not produce the same results, and are used in distinct contexts. MT ai
16、ms at assembling all the information necessary for translation in a software programme so that a text can be translated without human intervention whatsoever. It exploits the computers capacity to analyze the structure of a statement or sentence in the source language, break it down into easily tran
17、slatable elements and then create a statement with the same structure in 5the target language. CAT uses a number of tools to help the translator work accurately and quickly, the most important of which are terminology databases and translation memories. In effect, the computer offers a new way of ap
18、proaching text processing of both the source and target text. The technology basically acts as a recycler, offering the professional possible translations for the text hes working on, which are based on previous material. CAT is not capable of producing an immediately useable text, as languages are
19、highly dependant on context. Backed by a translation memory, CAT is considered mainly a save-time tool, rather than a replacement for human activity. It requires post-editing in order to yield a quality target text. In its simplest form, a translation memory is a database in which a translator store
20、s translations for future re-use, either in the same text or other texts. Basically the software records bilingual pairs: a source-language segment (usually a sentence) combined with a target-language segment. If an identical or similar source-language segment comes up later, the translation memory
21、will find the previously-translated segment and automatically suggest it for the new translation. The translator is free to accept it without change, 6edit it to fit the current context, or reject it altogether. 4.The Limits of Machine Translation Pure-machine translation can deliver acceptable resu
22、lts when dealing with very predictable technical texts, which never go beyond the expected domain of discourse. But this is little help in the domains where people want translation the most: in spontaneous conversations, in person, on the telephone and on the internet. Computers just do not have the
23、 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: “He drives too fast“ and “Patients must com
24、e for the blood test on fast“. A computer can be programmed to understand either of these examples, but not to distinguish between the two occurrences of “fast“. A pure-computer translation is similar to the one performed by a human without a deep knowledge of the target language. Grammatical rules
25、can be memorized, 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 choose between diverse meanings. Computers not only lack the knowledge 7of the world to deal with word choice, but they also lack the knowledge nec
26、essary for cultural sensitivity. Every single sentence usually contains a number of gross errors. If a translation done by a machine is accurate, it can be accurate only coincidentally because the machine does not understand the concept of accuracy. 5.Conclusion: Machine translation will beyond doub
27、t play its role in the coming future, helping to bring down the communication barriers in the newly interconnected world. Yet, it is up to translators to explain to the general public what machine translation is, what are its strengths and weaknesses, and what is its likely role in the future develo
28、pment of our civilization. Translators have an insight into this problem that other professionals can hardly be expected to have. References 1Readings in Machine Translation ，Sergei Nirenburg, Harold L. Somers Yorick A. Wilk ，The MIT Pre. 2Machine Translation: From Real Users to Research ,Robert E. Frederking . 3Bell, Roger T. Translation and Translating: Theory and Practice London and New York: Longman New mark, peter. 1991. 84Peter New mark, About Translation. Multilingual Matters Ltd., 1991. 5http:/en.wikipedia.org/wiki/Computer-assisted_translation.
