Finding a voice The Economist. Computer translations have got strikingly better, but still need human input. IN STAR TREK it was a hand held Universal Translator in The Hitchhikers Guide to the Galaxy it was the Babel Fish popped conveniently into the ear. In science fiction, the meeting of distant civilisations generally requires some kind of device to allow them to talk. High quality automated translation seems even more magical than other kinds of language technology because many humans struggle to speak more than one language, let alone translate from one to another. The idea has been around since the 1. MT. It goes back to the early days of the cold war, when American scientists were trying to get computers to translate from Russian. They were inspired by the code breaking successes of the second world war, which had led to the development of computers in the first place. To them, a scramble of Cyrillic letters on a page of Russian text was just a coded version of English, and turning it into English was just a question of breaking the code. Scientists at IBM and Georgetown University were among those who thought that the problem would be cracked quickly. Language Software For The Programmers Consortium DefinitionHaving programmed just six rules and a vocabulary of 2. New York on January 7th 1. Preface. This is a book about Natural Language Processing. By natural language we mean a language that is used for everyday communication by humans languages like. I MISRAC2004 Guidelines for the use of the C language in critical systems October 2004 Licensed to Tyler Doering. Copy 1 of 1. For my day job, Im the cofounder and CEO of Stack Overflow, the largest online community for programmers to learn, share their knowledge, and level up. Mi pyeryedayem mislyi posryedstvom ryechyi, which came out correctly as We transmit thoughts by means of speech. Leon Dostert of Georgetown, the lead scientist, breezily predicted that fully realised MT would be an accomplished fact in three to five years. Instead, after more than a decade of work, the report in 1. John Pierce, mentioned in the introduction to this report, recorded bitter disappointment with the results and urged researchers to focus on narrow, achievable goals such as automated dictionaries. Government sponsored work on MT went into near hibernation for two decades. What little was done was carried out by private companies. The most notable of them was Systran, which provided rough translations, mostly to Americas armed forces. Language Software For The Programmers Consortium' title='Language Software For The Programmers Consortium' />La plume de mon ordinateur. The scientists got bogged down by their rules based approach. Create Windows Task Scheduler Using Commas. Having done relatively well with their six rule system, they came to believe that if they programmed in more rules, the system would become more sophisticated and subtle. Instead, it became more likely to produce nonsense. Adding extra rules, in the modern parlance of software developers, did not scale. Besides the difficulty of programming grammars many rules and exceptions, some early observers noted a conceptual problem. The meaning of a word often depends not just on its dictionary definition and the grammatical context but the meaning of the rest of the sentence. Conclusions+-+POSIX+and+Recommendations.jpg' alt='Language Software For The Programmers Consortium Group' title='Language Software For The Programmers Consortium Group' />Yehoshua Bar Hillel, an Israeli MT pioneer, realised that the pen is in the box and the box is in the pen would require different translations for pen any pen big enough to hold a box would have to be an animal enclosure, not a writing instrument. How could machines be taught enough rules to make this kind of distinction They would have to be provided with some knowledge of the real world, a task far beyond the machines or their programmers at the time. Two decades later, IBM stumbled on an approach that would revive optimism about MT. Its Candide system was the first serious attempt to use statistical probabilities rather than rules devised by humans for translation. Statistical, phrase based machine translation, like speech recognition, needed training data to learn from. Candide used Canadas Hansard, which publishes that countrys parliamentary debates in French and English, providing a huge amount of data for that time. Aims. This module aims to familiarise the student with the basics of Computer Assisted Language Learning CALL, beginning with a descrption of what CALL is all. XQuery is a standardized language for combining documents, databases, Web pages and almost anything else. It is very widely implemented. It is powerful and easy to. List of freely available programming books Ada, Assembly, Basic, C, C, C, CGI, JavaScript, Perl, Delphi, Pascal, Haskell, Java, Lisp, PHP, Prolog, Python, Ruby. The phrase based approach would ensure that the translation of a word would take the surrounding words properly into account. But quality did not take a leap until Google, which had set itself the goal of indexing the entire internet, decided to use those data to train its translation engines in 2. Systran to its own statistics based system. To build it, Google trawled about a trillion web pages, looking for any text that seemed to be a translation of anotherfor example, pages designed identically but with different words, and perhaps a hint such as the address of one page ending in en and the other ending in fr. According to Macduff Hughes, chief engineer on Google Translate, a simple approach using vast amounts of data seemed more promising than a clever one with fewer data. Training on parallel texts which linguists call corpora, the plural of corpus creates a translation model that generates not one but a series of possible translations in the target language. The next step is running these possibilities through a monolingual language model in the target language. Language Software For The Programmers Consortium' title='Language Software For The Programmers Consortium' />This is, in effect, a set of expectations about what a well formed and typical sentence in the target language is likely to be. Single language models are not too hard to build. Parallel human translated corpora are hard to come by large amounts of monolingual training data are not. As with the translation model, the language model uses a brute force statistical approach to learn from the training data, then ranks the outputs from the translation model in order of plausibility. Statistical machine translation rekindled optimism in the field. Internet users quickly discovered that Google Translate was far better than the rules based online engines they had used before, such as Babel. Fish. Such systems still make mistakessometimes minor, sometimes hilarious, sometimes so serious or so many as to make nonsense of the result. And language pairs like Chinese English, which are unrelated and structurally quite different, make accurate translation harder than pairs of related languages like English and German. But more often than not, Google Translate and its free online competitors, such as Microsofts Bing Translator, offer a usable approximation. Such systems are set to get better, again with the help of deep learning from digital neural networks. The Association for Computational Linguistics has been holding workshops on MT every summer since 2. One of the events is a competition between MT engines turned loose on a collection of news text. In August 2. 01. 6, in Berlin, neural net based MT systems were the top performers out of 1. Now Google has released its own neural net based engine for eight language pairs, closing much of the quality gap between its old system and a human translator. This is especially true for closely related languages like the big European ones with lots of available training data. The results are still distinctly imperfect, but far smoother and more accurate than before. Translations between English and say Chinese and Korean are not as good yet, but the neural system has brought a clear improvement here too. The Coca Cola factor. Neural network based translation actually uses two networks. One is an encoder. Each word of an input sentence is converted into a multidimensional vector a series of numerical values, and the encoding of each new word takes into account what has happened earlier in the sentence. Marcello Federico of Italys Fondazione Bruno Kessler, a private research organisation, uses an intriguing analogy to compare neural net translation with the phrase based kind. The latter, he says, is like describing Coca Cola in terms of sugar, water, caffeine and other ingredients. By contrast, the former encodes features such as liquidness, darkness, sweetness and fizziness. Stroustrup FAQMorgan Stanley. Columbia University. Texas A M University. Churchill College, Cambridgehome. Modified Ocober 1, 2. These are questions that people ask me often. If you have better questions or comments on the answers. Please remember that I cant spend all of my time improving my homepages. This page concentrates on personal opinions and general questions related to. For questions that more directly relate to C language features and the. C, see. The C Foundations FAQ or. C style and technique FAQ. For C terminology and concepts, see my. For links to useful sources of C information, see. C page and. For information about my books incl. For papers and ISBNs for translations of my books, see. It can be difficult for non Scandinavians. The best suggestion I have heard yet was start by saying it a few times. Norwegian, then stuff a potato down your throat and do it again. For people who cant receive sound, here is a suggestion. Both of my names are pronounced with two syllables Bjar ne. Neither the B nor the J in my first name are stressed and the NE. Be ar neh or By ar ne would give an idea. The first U in my second name really should have been a V making the. Strov strup. The second U is a bit like the OO in OOP, but still short maybe. Strov stroop will give an idea. Yes, this probably is the most frequently asked question. P. S. My first name is Bjarne not Bjorn not a name. Bjrn a related but different name, nor Barney an unrelated name. My second name is Stroustrup not Stroustroup, Stroustrop, Strustrup. Strustrop, Strustroup, Straustrup, nor Straustroup documents using each of. I try to answer my email. However, please try to avoid asking a question that is answered in my. Also, please dont rely on a prompt answer. I get a lot of email. Here are links to. I do, but I get a lot of email. I estimate that I reply to more than 9. I receive. However, sometimes I get overwhelmed. Some messages are lost in my mailbox, some are delayed until I can find time. I get around to answering a set of related messages. Unfortunately, longer and more thoughtful messages are more likely to get. Also, if you mail me, please try to make sure that I can reply to you. I really hate it when I have written and sent a reply, just to find that the. Two kinds of messages have a relatively high chance of getting lost homework. I use this proprietary library. Im a bit sad about not answering the latter questions because often the. DOS, Windows, or whatever interface. C is not part of the C standard and I cannot keep up with the huge. C libraries. If you fail to receive an answer. Also, unless you give your name, I am now likely to delete the message unread. This is a new policy. I was never a great fan of pseudonyms. I find that the chance of a polite. Guy. 3 is getting too low for me to bother trying. Im a contents provider not a website designer. I can use my time to improve the contents or the looks, but not both. What looks cool and modern to someone is often considered bad. Most of the newsgroup postings, interviews, etc. The obvious exception is the infamous IEEE interview. I find rather unfunny. I have been known to post to newsgroups from my gmail account where my user id is Bjarne. If in doubt, consider the style and contents of the suspect message, check for other. Classes are there to help you organize your code and to reason about your programs. You could roughly equivalently say that classes are there to help you avoid making mistakes. In this way, classes significantly helps maintenance. A class is the representation of an idea, a concept, in the code. An object of a class represents. Without classes, a reader of the code would have. With classes, more of the high level structure of your. A well designed class presents a clean and simple interface to its users, hiding its. If the representation shouldnt be hidden say, because users should be able to change. Note that even data structures can benefit from auxiliary functions, such as constructors. When designing a class, it is often useful to consider whats true for every object of the. Such a property is called an invariant. For example, the invariant. It is the job of every constructor to. Every member function must leave the invariant valid upon exit. This kind of thinking is particularly useful for classes that manage resource. For example, a file handle class will have the invariant. The file handle constructor opens the file. Destructors free resources acquired by constructors. For example, the destructor for. Filehandle. Filehandleconst char n, const charrw. Openfailuren. Filehandle fclosef destructor. If you havent programmed with classes, you will find parts of this explanation obscure and. Look for examples. Like all good textbooks. TCPL has lots of examples. For a less detailed and easier to approach book, see. Most modern C libraries consist among other things of classes and a library tutorial. There are lots of definitions of object oriented, object oriented programming. For a longish explanation of what. I think of as object oriented, read. Why C isnt just an object oriented programming language. That said, object oriented programming is a style of programming originating with. Simula more than 4. In the context of C and many other languages with their roots in Simula. See Whats so great about classes The point about arranging classes into a class hierarchy. To really understand OOP, look for some examples. For example, you might have. Driver common driver interface. Status check 0 read status. This Driver is simply an interface. It is defined with no data members and a set of pure virtual functions. A Driver can be used through this interface and many different kinds of drivers. Driver. 1 public Driver a driver. Driver. 1Register constructor. Status check. implementation details, incl. Driver. 2 public Driver another driver. Driver. 2Register. Status check. implementation details, incl., representation. Note that these drivers hold data state and objects of them can be created. They implement the functions defined in Driver. We can imagine a driver being used like this. Driver d use driver. Status oldstatus d. The key point here is that f doesnt need to know which kind of driver it uses. Driver. that is, an interface to many different kinds of drivers. We could invoke f like this. Driver. 1 d. 1Register0xf. Driver. 1 for device. Driver. 2 d. 2Register0xa. Driver. 2 for device. Note that when f uses a Driver the right kind of operations are implicitly chosen at run time. For example, when f is passed d. Driver. 1 read, whereas when f is passed d. Driver. 2 read. This is sometimes called run time dispatch or dynamic dispatch. In this case there is no way that f could know the kind of device it is called with because. Please note that object oriented programming is not a panacea. OOP does not simply mean good. The strength of OOP. OOP is that too many people try to force too many problems into a hierarchical mould. Not every program should be object oriented. As alternatives, consider. C, and Fortran. Generic programming is programming based on parameterization. You can parameterize a type with another. The aim of generic programming is to generalize a useful algorithm or data. For example, a vector of integers is fine and so is. However, a generic solution that.