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MIT Technology Review (BUTTON) Menu * Topics + o Business Impact o Connectivity o Intelligent Machines o Rewriting Life o Sustainable Energy + o 10 Breakthrough Technologies o 35 Innovators Under 35 o 50 Smartest Companies + Views + Views from the Marketplace * The Download * Magazine * Events * More + Video + Special Publications + MIT News magazine + Newsletters + Help/Support + Advertise with Us * Log in / Create and account * Subscribe * Log in / Create an account * Search * ____________________ Submit Click search or press enter Business Impact Human translators are still on top—for now Machine translation works well for sentences but turns out to falter at the document level, computational linguists have found. * by Emerging Technology from the arXiv * September 5, 2018 * * * * * * * * * * * * You may have missed the popping of champagne corks and the shower of ticker tape, but in recent months computational linguists have begun to claim that neural machine translation now matches the performance of human translators. Recommended for You 1. A neural network can learn to organize the world it sees into concepts—just like we do 2. One day your voice will control all your gadgets, and they will control you 3. Never mind killer robots—here are six real AI dangers to watch out for in 2019 4. Google Assistant now comes with a real-time translator for 27 languages 5. When Chinese hackers declared war on the rest of us The technique of using a neural network to translate text from one language into another has improved by leaps and bounds in recent years, thanks to the ongoing breakthroughs in machine learning and artificial intelligence. So it is not really a surprise that machines have approached the performance of humans. Indeed, computational linguists have good evidence to back up this claim. But today, Samuel Laubli at the University of Zurich and a couple of colleagues say the champagne should go back on ice. They do not dispute their colleagues’ results but say the testing protocol fails to take account of the way humans read entire documents. When this is assessed, machines lag significantly behind humans, they say. [machine-translation.png?sw=600&cx=0&cy=0&cw=1420&ch=53 5] At issue is how machine translation should be evaluated. This is currently done on two measures: adequacy and fluency. The adequacy of a translation is determined by professional human translators who read both the original text and the translation to see how well it expresses the meaning of the source. Fluency is judged by monolingual readers who see only the translation and determine how well it is expressed in English. Computational linguists agree that this system gives useful ratings. But according to Laubli and co, the current protocol only compares translations at the sentence level, whereas humans also evaluate text at the document level. So they have developed a new protocol to compare the performance of machine and human translators at the document level. They asked professional translators to assess how well machines and humans translated over 100 news articles written in Chinese into English. The examiners rated each translation for adequacy and fluency at the sentence level but, crucially also at the level of the entire document. The results make for interesting reading. To start with, Laubli and co found no significance difference in the way professional translators rated the adequacy of machine- and human-translated sentences. By this measure, humans and machines are equally good translators, which is in line with previous findings. However, when it comes to evaluating the entire document, human translations are rated as more adequate and more fluent than machine translations. “Human raters assessing adequacy and fluency show a stronger preference for human over machine translation when evaluating documents as compared to isolated sentences,” they say. The researchers think they know why. “We hypothesise that document-level evaluation unveils errors such as mistranslation of an ambiguous word, or errors related to textual cohesion and coherence, which remain hard or impossible to spot in a sentence-level evaluation,” they say. For example, the team gives the example of a new app called “微信挪 车,” which humans consistently translate as “WeChat Move the Car” but which machines often translate in several different ways in the same article. Machines translate this phrase as “Twitter Move Car,” “WeChat mobile,” and “WeChat Move.” This kind of inconsistency, say Laubli and co, makes documents harder to follow. This suggests that the way machine translation is evaluated needs to evolve away from a system where machines consider each sentence in isolation. “As machine translation quality improves, translations will become harder to discriminate in terms of quality, and it may be time to shift towards document-level evaluation, which gives raters more context to understand the original text and its translation, and also exposes translation errors related to discourse phenomena which remain invisible in a sentence-level evaluation,” say Laubli and co. That change should help machine translation improve. Which means it is still set to surpass human translation—just not yet. Ref: arxiv.org/abs/1808.07048 : Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation Learn from the humans leading the way in emerging technology at EmTech Next. Register Today! June 11-12, 2019 Cambridge, MA Register now (BUTTON) (BUTTON) Share * * * * * * Tagged emerging technology, arXiv Emerging Technology from the arXiv Emerging Technology from the arXiv Emerging Technology from the arXiv covers the latest ideas and technologies that appear on the Physics arXiv preprint server. It is part of the Physics arXiv Blog. Email:… More KentuckyFC@arxivblog.com Subscribe to the Physics arXiv Blog RSS Feed. Related Video More videos [Xb-logo-circle.png?sw=75] Business Impact Finding the balance of human intelligence and artificial intelligence 00:53 [Xb-logo-circle.png?sw=75] Business Impact How does the customer experience change when you're in a world of conversation? 00:39 [Xb-logo-circle.png?sw=75] Business Impact Trump's Deputy CTO on immigrant workers 02:27 [Xb-logo-circle.png?sw=75] Business Impact A View from the White House 23:50 Recommended for You 1. A neural network can learn to organize the world it sees into concepts—just like we do 2. 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