Unit 7 Machine Translation全新版大学英语综合教程五课文翻译.doc

Unit 7 Machine TranslationText A Tongues of the Web 1 Since its earliest days, machine translation the use of computers to translate documents from one language to another automatically has suffered from exaggerated claims and impossible expectations. One characteristic (but apocryphal) tale tells of an American military system designed to translate Russian into English, which is said to have rendered the famous Russian saying The spirit is willing but the flesh is weak into The vodka is good but the meat is rotten. 2 This sort of joke prompts a hollow laugh from those in the machine-translation (MT) business. It does so because it demonstrates both the difficulty of getting computers to understand human languages, and the high expectations that must be met if MT is to be taken seriously. Over the years, there have been a number of promising new approaches in the field, and ever-cheaper processing and storage technology have helped improve things. But progress has been painfully slow, and the decisive breakthrough that will transform the fortunes of MT has never appeared. 3 Now the Internet has given MT a much needed shot in the arm. This is odd because the ability to transmit information quickly and cheaply would not, on the face of it, appear to make the process of translation any easier. Yet, although the underlying technology of MT is still the same as it ever was, the rise of the Internet changes the way in which technology is perceived and the way it is used. And there are signs that, in future, it could improve the way it works as well. 4 The idea of automating the process of translation using computers goes back to the late 1940s. Warren Weaver of the Rockefeller Foundation in New York wrote a memorandum suggesting that the code-breaking successes of the second world war, combined with electronic computers and the new information theory laid out by Claude Shannon, might form the basis of an automatic translation system. This prompted research at several American universities, and the first public demonstration of MT the result of a collaboration between IBM and Georgetown University took place in 1954. This early system, based on a simple bilingual dictionary with a few rules to determine word order, caused a surge of enthusiasm and funding. 5 For the next decade, MT researchers tried to overcome the limitations of simple dictionary-based systems using more complex approaches which analysed the source text using grammatical rules. Today, the computer, or electronic brain, is well along toward picking up the burden of machine translation, declared the Atlantic Monthly in 1959. But despite such optimism, progress was slow, and in 1964 the American government established a committee to examine the prospects for MT. Its report, issued two years later, concluded that, compared with human translators, MT systems were slower, less accurate, and twice as expensive. Instead, the committee recommended that research should concentrate on devising systems to assist human translators, rather than trying to replace them altogether. As a result, American funding for pure MT research dried up. 6 In some fields, however, it was recognised that even a rough-and-ready translation was better than none at all. Systran, a company established by Peter Toma, a researcher at the California Institute of Technology in Pasadena, sold a Russian-to-English translation system to the United States Air Force in 1970, and the same system was subsequently adopted by the European Commission. During the 1970s, demand for translation systems began to emerge in the business community. 7 During the 1980s, the combination of rapid falls in the cost of computing power and increasing demand from governments and multinational companies caused a revival of interest in MT, spurring renewed research. New systems were developed. Many of them worked by translating the source text into an intermediate language or symbolic representation, from which it could be translated into any of several other languages. As computers became more powerful and storage became cheaper, other new approaches emerged in the 1990s: analysis of parallel texts (the same text in two languages) led to new statistical-translation systems, which did not rely on any underlying grammatical rules, and to example-based systems which translated one sentence at a time by searching a database for examples of similar sentences whose translations were known. 8 Even so, the quality of MT has not really improved very much over the past three decades, says John Hutchins, an expert on the history of machine translation at the University of East Anglia, in Britain. If you look at quality of output now, compared with 1970, in many cases you cant see much improvement, he says. What has changed is that MT systems have now been plugged into the Internet. That changes the way they are used, and the expectations of them. 9 The Internet has democratised MT and boosted demand dramatically, as users around the world struggle to understand pages in languages other than their own. And as companies set up increasingly elaborate websites, they have become aware of the need to maintain multiple sites in different countries and serve customers in different languages. Of Americas 100 largest firms, 33 had multilingual websites at the end of 1999, and 57 did a year later. A study by Aberdeen Group, a management consultancy, found that, on average, users spend up to twice as long at a site, and are four times more likely to buy something from it, if it is presented to them in their own language. Another study by IDC, a technology consultancy, found that only 5% of the 50 top websites responded appropriately to e-mail queries in a foreign language; most simply asked for the message to be resent in English. All of which highlights the need for MT systems to provide on-the-fly translations, and for elaborate publishing systems that can manage multilingual websites. 10 Arguably the best known online MT system is Babel Fish, which relies on Systran software to translate pages retrieved by the Alta Vista search engine. Anyone who has used Babel Fish will be familiar with the unintentional hilarity of the results; one popular game involves scrambling the lyrics of pop songs by translating them from English into another language and then back again (a round-trip translation). Other MT systems are also in use online, providing rough-and-ready translations of chat-room conversations and e-mail messages. Demand for such services is likely to increase as the diversity of Internet users increases. At the end of 2000, 48% of Internet users were English speakers, but this figure is expected to fall to 32% by the end of 2002. 11 Unfortunately, MT systems work best when they have been customised for a particular subject area, such as microbiology, aerospace or particle physics. This involves analysing typical documents and adding common words and technical terms to the systems dictionary. Using MT to translate Internet pages, which can be about anything at all, therefore produces terrible results, since no customisation is possible. To make matters worse, most MT systems were designed for use with high quality documents, whereas many web pages, chat-rooms and e-mails tend to involve slang, colloquial language and ungrammatical constructions. 12 Even so, Steve McClure, an analyst at IDC, notes that the Internet has refocused MT from being a tool that provides a first draft for translators to becoming a general tool for gaining a quick, partial understanding of perishable texts in high-volume environments without human involvement in the translation process. The Internet changes the game for machine translation: users want speed, rather than quality, and are more likely to accept poor results.网络语言佚名 自问世之初，对于机器翻译 即运用计算机自动把文件从一种语言译成另一种语言就有人言过其实地下断言，就有人寄予不切实际的期望。一则典型的(但系杜撰的)故事讲到美国军方一个专为俄译英设计的系统，据说它把著名的俄罗斯成语“心有余而力不足”译作“酒香而肉臭”。 从事机器翻译的业内人士听了这类笑话顶多苦笑一声。这是因为，这类故事表明要计算机理解人类语言是多么困难，也表明如果要人们真把机器翻译当回事，必须满足的期望有多么高。多年来，该领域开辟了不少颇具发展前途的新途径，越来越便宜的处理和存储技术帮助作出了一些改进。但发展极其缓慢，将会改变机器翻译命运的决定性的突破尚未出现。 如今因特网给了机器翻译以亟需的推动力。这未免奇怪，因为从表面看来，迅速而又便宜地传送信息的能力似乎不会使翻译的过程变得容易些。然而，虽然机器翻译的主要技术并无任何改进，因特网的崛起却改变了对技术的理解与使用。有迹象表明，在将来，机器翻译技术也将改进其目前的工作方法。 利用计算机使翻译自动化的想法始于20世纪40年代后期。纽约洛克菲勒基金会的沃伦韦弗写了一份备忘录，说若能将第二次世界大战中成功的密码破译技术与电子计算机以及克劳德香农提出的新“信息理论”相结合，即能构成自动翻译系统的基础。这一意见促使美国若干大学展开研究，并于1954年举行了机器翻译的第一次公开展示 国际商用机器公司和乔治敦大学合作的结果。这一早期系统以一本由若干条规则决定词序的简单双语词典为基础，当时引起一阵热潮并引来一些研究基金。 在以后的十年间，机器翻译研究人员试图突破以字典为基础的简单系统的限制，使用更复杂的方法，用语法规则分析原语文本。“如今，计算机，或者说电脑，渐渐地挑起机器翻译这副重担，”1959年大西洋月刊如此宣称。然而，尽管态度如此乐观，进展却相当缓慢，1964年，美国政府建立了一个委员会试图探明机器翻译的前景。该委员会两年之后发布的报告断定，与人工翻译相比，机器翻译系统速度慢，准确率低，代价要高一倍。于是，该委员会建议机器翻译研究应致力于开发能帮助人工翻译的系统，而非试图完全取代人工翻译。结果美国纯粹机器翻译研究的资金来源枯竭了。 然而，人们认识到，在某些领域，即便是粗糙的翻译也聊胜于无。1970年，帕萨迪纳市加利福尼亚理工学院的研究人员彼得?托马建立的系统分析翻译家公司将一项俄英文翻译系统出售给美国空军，这一系统后来又被欧洲委员会采用。在20世纪70年代，商界对翻译系统的需求开始增长。 到了20世纪80年代，计算机价格的迅速下跌以及政府和跨国公司日益增长的需求重新激活了对机器翻译的兴趣，激励人们继续研究。新的系统得到开发。不少新系统将原语文本变成某种可译为几种其他语言的中介语或符号系统。随着计算机的功能越来越大，存储器的价格越来越便宜，20世纪90年代出现了其他一些新方法：对平行文本的分析(用两种语言表达的同一文本)产生了不依赖任何基本语法规则的基于统计的机器翻译新系统，还产生了基于例句的翻译系统，即通过在数据库中搜索已有现成翻译的类似例句一次翻译一句句子。 英国东英格兰大学一位机器翻译史专家约翰哈钦斯说，即便如此，在过去的三十年里，机器翻译的质量并没有得到多少改进。“如果你看一看如今的译文质量