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In this two-part article we explore why translating academic texts is easier than proofreading them<\/em><\/span><\/p>\r\n\r\n\r\n\r\n <\/p>\r\n\r\n\r\n\r\n As an author who has needed an article translated, maybe the following thought has occurred to you: \u201cWouldn\u2019t it save time and make things easier for everyone if I ran my article through Google Translate first and then only need to have it proofread?\u201d<\/p>\r\n\r\n\r\n\r\n On the face of it, there seem to be quite a few benefits:<\/p>\r\n\r\n\r\n\r\n :::<\/span><\/p>\r\n\r\n\r\n\r\n These points are all valid to a certain extent, but in the following two-part post we\u2019re going to explore why so-called \u201cmachine translation\u201d (i.e. Google Translate & Co) makes absolutely no sense for academic translation and can actually end up being a lot more work for everyone involved. In the first part, we will show how Google Translate works since it is the most popular machine-translation tool and then look at one specific problem that arises for academic texts in regard to vocabulary. In the second part of the post, we will then discuss sentence structure and how machine translation can render a text so unclear as to make it nearly impossible to proofread accurately.<\/p>\r\n\r\n\r\n\r\n All Roads Lead Through English<\/strong><\/p>\r\n\r\n\r\n\r\n First let\u2019s discuss one term that sheds light onto the difference between proofreading a text translated by a human translator and one translated by a machine. In the translation business, if you revise a text that was translated by a machine, you are not \u201cproofreading.\u201d Instead, you are \u201cpost-editing.\u201d This should be an indication to laypersons that the essence of what is done is different enough to deserve a different name. Proofreading often involves looking for typographical errors, consistency in terminology, or stylistic concerns. Post-editing, in contrast, involves making many inferences about what the source text might have intended when the text to be edited is ambiguous or completely unclear.<\/p>\r\n\r\n\r\n\r\n To understand why the text that results from a machine translation can be more ambiguous or unclear than a human translation, we will look at one key point of how at least the most common machine translation tool works. This will demonstrate why it may work wonderfully for a translation of that e-mail you need to send to a British or French colleague about what hotel to book for your next conference but fails miserably when it comes to your journal article.<\/p>\r\n\r\n\r\n\r\n When you put a text into Google Translate, it uses statistical machine translation to translate your text, meaning that it looks at all the translated documents available to it and finds the best matches. That sounds nice in theory, and in simple sentences it works fairly well, particularly when English is one of the languages. Let\u2019s take a look at what happens when we take a fairly simple German e-mail such as described above and have it translated into English and French.<\/p>\r\n\r\n\r\n\r\n <\/p>\r\n ich schaue gerade nach Hotels in Amsterdam. Hast du schon ein Zimmer reserviert? Das Konferenzhotel ist mir zu teuer – es w\u00e4re sch\u00f6n, wenn wir im selben Hotel w\u00e4ren, dann k\u00f6nnten wir zusammen hinfahren.<\/p>\r\n <\/p>\r\n Viele Gr\u00fc\u00dfe,<\/p>\r\n Max<\/p>\r\n<\/td>\r\n <\/p>\r\n I just look for hotels in Amsterdam. Have you made a reservation? The conference hotel is too expensive – it would be nice if we were in the same hotel, we could go together.<\/p>\r\n <\/p>\r\n Best wishes,<\/p>\r\n Max<\/p>\r\n <\/p>\r\n<\/td>\r\n <\/p>\r\n Je regarde juste pour h\u00f4tels \u00e0 Amsterdam. Avez-vous fait une r\u00e9servation? Cet h\u00f4tel de conf\u00e9rence est trop cher – ce serait bien si nous \u00e9tions dans le m\u00eame h\u00f4tel, nous pourrions aller ensemble.<\/p>\r\n <\/p>\r\n Meilleures salutations,<\/p>\r\n Max<\/p>\r\n <\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n\r\n\r\n\r\n The English is pretty good. The first sentence uses the simple present instead of the present progressive or present perfect progressive (\u201cI just look for\u2026\u201d should be either \u201cI\u2019m looking for\u2026\u201d or \u201cI\u2019ve been looking for\u2026\u201d), but the meaning is clear and the British colleague would understand the e-mail with no problems.<\/p>\r\n\r\n\r\n\r\n Now let\u2019s look at the same German e-mail translated into French. The main problem is in the second sentence. The German reads: \u201cHast du<\/u> schon ein Zimmer reserviert?\u201d (\u201cHave you [informal] already reserved a room?\u201d) The French, though uses the formal \u201cAvez-vous\u201d, reserved for use between people who are not on a first-name basis. Why would Google Translate have done this? The reason is simple: when translating between two non-English languages, Google Translate first translates a text into English and then into the other foreign language <\/strong>(German -> English -> French). Because the English \u201cyou\u201d is ambiguous and can be either \u201ctu\u201d or \u201cvous,\u201d Google needs to make a decision, and here it chose incorrectly. Imagine that on a more abstract scale. An unambiguous German word is changed into an ambiguous English one, and then that English one is translated into the word in your target language (in this case French). The potential for incorrect choices abound. Even when staying within a pair that includes English, however, decisions on sentence structure and vocabulary need to be made constantly by the machine. Now we will turn to one language pair, German to English, and look at the problems that arise when working with academic texts.<\/p>\r\n\r\n\r\n\r\n Problems Increase with Academic Texts: Vocabulary<\/strong><\/p>\r\n\r\n\r\n\r\n A second point on how Google Translate works is that it bases its choices on statistical frequency, whereas many academic texts distinguish themselves by a particular jargon that is specific to the discipline but not common in regular speech or writing. Just taking one word as an example, let us look at the German \u201cGestalt\u201d \u2013 in a casual sentence, \u201cGestalt\u201d often means \u201cfigure\u201d or \u201cshape,\u201d and Google Translate correctly translates the word in the sentence \u201cHast du die Gestalt gesehen?\u201d as \u201cfigure.\u201d But in the field of psychology, \u201cGestalt\u201d is a specific term that is left untranslated in English (\u201cgestalt psychology\u201d, for example). When using Google Translate, the sentence:<\/p>\r\n\r\n\r\n\r\n\r\n
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\r\n German<\/strong><\/td>\r\n English<\/strong><\/td>\r\n French<\/strong><\/td>\r\n<\/tr>\r\n \r\n Lieber John,\r\n Dear John,\r\n Dear John,\r\n