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 Hitchhiker’s 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. Having programmed just six rules and a vocabulary of 2. New York on January 7th 1. 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 America’s armed forces. La plume de mon ordinateur. The scientists got bogged down by their rules- based approach. 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 grammar’s 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. . 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. ![]() Access-Freak :: Getting started with Microsoft(R) Access 2007 (Step by Step Tutorials/Samples.). Tutorial on doing a complete Sysprep on a Windows 7 Machine from start to finish; copyprofile=true, automatically activating windows, etc. The Economist offers authoritative insight and opinion on international news, politics, business, finance, science, technology and the connections between them. 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 Canada’s Hansard, which publishes that country’s parliamentary debates in French and English, providing a huge amount of data for that time. 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 another—for 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. Street Fighter 4 Full Free Game Downloading For Pc . 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. 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 mistakes—sometimes 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 Microsoft’s 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 Italy’s 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. Once the source sentence is encoded, a decoder network generates a word- for- word translation, once again taking account of the immediately preceding word. This can cause problems when the meaning of words such as pronouns depends on words mentioned much earlier in a long sentence. This problem is mitigated by an “attention model”, which helps maintain focus on other words in the sentence outside the immediate context. Neural- network translation requires heavy- duty computing power, both for the original training of the system and in use. The heart of such a system can be the GPUs that made the deep- learning revolution possible, or specialised hardware like Google’s Tensor Processing Units (TPUs). Smaller translation companies and researchers usually rent this kind of processing power in the cloud. But the data sets used in neural- network training do not need to be as extensive as those for phrase- based systems, which should give smaller outfits a chance to compete with giants like Google.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |