论文机器翻译风险是适合不知如何写机器翻译方面的相关专业大学硕士和本科毕业论文以及关于常见的机器翻译软件有?论文开题报告范文和相关职称论文写作参考文献资料下载。
The ideal translator is a person “on whom nothing is lost,” said Henry James. Or maybe it’s a machine. But a machine won’t stop you from swearing at nuns...
Years ago, on a flight from Amsterdam to Boston, two American nuns seated to my right listened to a voluble1 young Dutchman who was out to discover the United States. He asked the nuns where they were from. Alas, Framingham, Massachusetts was not on his itinerary, but, he noted, he had“shitloads of time and would be visiting shitloads of other places”.2
The jovial young Dutchman had apparently gathered that“shitloads” was a colourful synonym for the bland “lots”.3 He had mastered the syntax of English and a rather extensive vocabulary but lacked experience of the appropriateness of words to social contexts.4
This memory sprang to mind with the recent news that the Google Translate engine would move from a phrase-based system to a neural network. Both methods rely on training the machine with a “corpus”5 consisting of sentence pairs： an original and a translation. The computer then generates rules for inferring, based on the sequence6 of words in the original text, the most likely sequence of words from the target language.
The procedure is an exercise in pattern matching. Similar pattern-matching algorithms are used to interpret the syllables you utter when you ask your smartphone to “navigate to Brookline” or when a photo app tags your friend’s face.7 The machine doesn’t “understand” faces or destinations； it reduces them to vectors8 of numbers, and processes them.
I am a professional translator, having translated some 125 books from the French. One might therefore expect me to bristle9 at Google’s claim that its new translation engine is almost as good as a human translator, scoring 5.0 on a scale of 0 to 6, whereas humans average 5.1. But I’m also a PhD in mathematics who has developed software that “reads” European newspapers in four languages and categorises the results by topic. So, rather than be defensive about the possibility of being replaced by a machine translator, I am aware of the remarkable feats of which machines are capable, and full of admiration for the technical complexity and virtuosity of Google’s work.10
My admiration does not blind me to the shortcomings of machine translation, however. Think of the young Dutch traveler who knew “shitloads” of English. The young man’s fluency demonstrated that his “wetware”—a living neural network, if you will—had been trained well enough to intuit the subtle rules （and exceptions） that make language natural.11 Computer languages, on the other hand, have context-free grammars. The young Dutchman, however, lacked the social experience with English to grasp the subtler rules that shape the native speaker’s diction, tone and structure. The native speaker might also choose to break those rules to achieve certain effects. If I were to say “shitloads of places”rather than “lots of places” to a pair of nuns, I would mean something by it. The Dutchman blundered into inadvertent comedy.12
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参考文献：
1、 应对系统性金融风险需要系统性策略 最近几年，金融风险持续在股票、债券、理财产品、房地产、外汇和互联网金融等市场与行业游走，引发了对爆发系统性金融危机的担忧。监管部门相继出台多份金。
2、 证券公司信息系统风险导向型审计实务 中国内部审计协会2009年1月1日正式发布施行的《内部审计具体准则第28号—信息系统审计》对证券公司信息系统内部审计工作具有重要的指导意义。本文。
3、 煤炭企业合同管理风险和内部审计 煤炭作为我国的主要能源之一，在经济发展中占有重要的战略地位。面对不断变化的市场环境，煤炭企业也顺应市场发展以及我国的战略规划逐步进行资源整合、做。
4、 防控国家审计风险之见 一、国家审计风险存在的主要环节（一）程序风险审计法对每一个审计项目从项目计划、审计通知、实施审计、出具审计报告、下达审计决定的全过程都有严格。
5、 审计视角下防范地方政府债务风险措施 一、目前我国地方政府债务的潜在风险我国政府高度重视地方政府债务管理，2014年9月，国务院印发《关于加强地方政府性债务管理的意见》，全面部署加。