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Lingvanex price12/27/2022 ![]() INSECTS - The Struggle is RealĪny sensitive reader of Kafka’s German might object that the machines have all failed to render the sentence’s primary literary effect, the triple alliteration of the negative prefixes “unruhigen … ungeheueren … Ungeziefer” – but then no human translator (HT) has ever managed to convey this effect, either. Generally speaking these MT engines converge on a single solution and it is acceptable. IBM Watson Language Translator's Translation of Franz Kafka's "The Metamorphosis" (Original: "Die Verwandlung") | © IBM Watson Language TranslatorNow we have no reason to expect that these engines will do particularly well on literary examples because they have not been trained to work on this kind of material, but thankfully such disasters are few and far between nowadays. PROMT Translation of Franz Kafka's "The Metamorphosis" (Original: "Die Verwandlung") | © PROMTThe wooden spoon goes to the IBM Watson Language Translator demo: PROMT makes a hash of the second half of the sentence: Some of the lesser used engines introduce a few interesting lexical variations – SYSTRAN Translate turns Gregor into a “monstrous pest”, LingvaNex Translator transforms him into a “tremendous vermin” – and there are still a few egregious outliers that give the whole process a bad name. What’s more, an apparently insignificant “tweak” to the input – like changing the spelling of Kafka’s “ungeheueren” to the contemporary norm “ungeheuren”, or even just omitting the full stop at the end of the sentence – can make a significant difference to the output. It should be emphasised that these solutions (checked in the course of June to September 2020) are not definitive because the systems are constantly evolving, thanks to programming upgrades and the input of new training examples, not least from end users. ![]() With minor word order changes it is also the same as the outputs from DeepL Translator and Reverso PONS makes a word order change and substitutes “troubled” for “restless”, but again is otherwise nearly identical. Google Translate's version of Franz Kafka's "The Metamorphosis" (Original: "Die Verwandlung") | © Google TranslateThis translation is identical to the output from Google Translate’s closest rival, Microsoft (Bing) Translator, and another of the big players, Yandex Translate. Here is the market leader, Google Translate: If we run this sentence through the available free online MT systems, a surprising degree of consensus emerges. ![]() For the most part it is quite clear what this sentence means, although one would expect there to be variation and uncertainty over the exact nature of the “ungeheueren Ungeziefer” that Gregor is transformed into, since Kafka keeps it deliberately vague. It is grammatically well formed, not particularly long or complex, and there are few snares to trap the unwary MT engine: it is unlikely that “Gregor Samsa” will fail to be recognised as a name,“eines Morgens” is a perfectly standard genitive adverbial time phrase, and so on. The story that this opening sentence is telling is, of course, deeply odd, but from a linguistic point of view the sentence is quite unexceptional. Als Gregor Samsa eines Morgens aus unruhigen Träumen erwachte, fand er sich in seinem Bett zu einem ungeheueren Ungeziefer verwandelt.įranz Kafka: "The Metamorphosis" (Original: "Die Verwandlung", 1915) Just how good are computers at literary translation at the moment? To begin to answer that question I tasked today’s leading MT systems with producing an English version of one of the most famous sentences in the German language, the opening of Kafka’s story “Die Verwandlung” (1915): We underestimate at our peril just how good computers have already become, and how quickly they are progressing, but they still have a long way to go, so now is a good juncture to survey the scene. Some kinds of literary text in plainer prose style can already be translated moderately well (for the commonest language pairs) by computers, and that trend is only set to intensify. ![]() With the rapid development of neural machine translation, literary translation scholars (and, to a lesser extent, literary translators themselves) are increasingly acknowledging that this position is untenable, and joining commercial and technical translators in anticipating that what the future holds for literary translators, too, is a role as post-editors of machine-translated output. Literary translators have long used computers for basic assistance, for example in the form of online dictionaries and corpora, but they have also long been resistant to the idea that machine translation (MT) – or even computer-assisted translation tools such as translation memory – can have any significant role to play in literary translation. ![]()
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