Translating with Wordfast

Wordfast is Translation Memory (TM) tool designed for corporations, translation agencies and translators. WF runs on multiple platforms (Windows, Mac, Linux, etc.), and opens a wide variety of formats including complex desktop publishing formats. WF integrates powerful tools for Project Managers for pre- and post-translation.

Wordfast Classic (WFC) is a CAT tool designed as a Microsoft Word™ add-on. Its lightweight, flexible structure makes it easy to install and use. It is designed to meet the specific needs of the individual translator and translation workgroups that primarily use Microsoft Word to translate. WFC maintains compatibility with Trados and most other CAT tools.

WFC offer demo modes that let translators perform small production jobs for evaluation (up to 500 segments; emptying the TM/database lets you repeat the demo mode indefinitely). WFS’ demo mode allows up to three connected users for free.

Wordfast Classic (WFC)

Wordfast Classic is a set of macros that run in Microsoft Word 97 or higher on any platform. More recent versions support features only available on higher versions of Microsoft Word, but generally still runs on Word 97. A document translated in Wordfast Classic is temporarily turned into a bilingual document (contains both source text and translation, in delimited segments), turning into its final form by being cleaned up. This workflow is similar to the old Trados 5, WordFisher and Logoport.

The Version 1 of Wordfast Classic was called Wordfast version 1, and was developed by Yves Champollion.  Version 2 was used by the translation agency Linguex, which acquired a 9-month exclusive usage right for their in-house staff and affiliated freelancers in late 1999. During this time Wordfast was expanded with features such as rule-based and glossary quality control, and network support. After the demise of Linguex, version 3 of Wordfast was released to the public, as a free tool with mandatory registration.

Initially, version 3 was free, with mandatory registration, using a serial number generated by the user’s computer. In October 2002, Wordfast became a commercial product with three-year licenses at a price of EUR 170 for users from « rich » countries and EUR 50 (later EUR 85) for users from other countries.

Wordfast Classic can handle the following formats: any format that Word can read, including plain text files, Word documents (DOC/DOCX), Excel (XLS/XLSX),  (PPT/PPTX), (RTF), tagged RTF and HTML. It does not offer direct support for OpenDocument formats because the current versions of Microsoft Word do not have import filters for OpenDocument files.

Wordfast Anywhere

Wordfast Anywhere is a free web-based version of Wordfast, with a workflow and user interface similar to that of Wordfast Classic. 

Although the service is free, certain restrictions apply:

  • No more than 10 source files simultaneously
  • No more than 1 million translation units per account
  • No more than 100 000 translation units per translation memory
  • No more than 100 000 glossary entries per account
  • Upload size limitation is 2 MB, but files can be uploaded in zipped format

Wordfast Anywhere’s privacy policy is that all uploaded documents remain confidential and are not shared. Users can optionally use machine translation and access a large read-only public translation memory.

In addition to being usable on tablets such as Windows Mobile, Android and Palm OS, Wordfast Anywhere is also available as an iPhone app. Since April 2011, Wordfast Anywhere has built-in optical character recognition of PDF files.

Wordfast Anywhere can handle Word documents (DOC/DOCX), Microsoft Excel (XLS/XLSX), PowerPoint (PPT/PPTX), Rich Text Format (RTF), Text (TXT), HTML, InDesign(INX), FrameMaker (MIF), TIFF (TIF/TIFF) and both editable and OCR-able PDF. It does not offer support for OpenDocument formats.

What is Neural Machine Translation (NMT)

Last year professionals had talked much about NMT. What is NMT?

Neural machine translation (NMT) is a machine translation approach that uses a large artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

All of the machine translation products (websites or apps) were based on algorithms using statistical methods to try to guess the best possible translation for a given word. This technology is called statistical machine translation.

However, one of the limitations of statistical machine translation is that it only translates words within the context of a few words before and after the translated word. For small sentences, it works pretty well. For longer ones, the translation quality could vary.

Now we have a new machine learning technology called deep learning or deep neural networks, one that tries to mimic how the human brain works (at least partially).

At a high-level, neural network translation works with in two stages:

— A first stage models the word that needs to be translated based on the context of this word (and its possible translations) within the full sentence, whether the sentence is 5 words or 20 words long.

— A second stage then translates this word model (not the word itself but the model the neural network has built of it), within the context of the sentence, into the other language.

One way to think about neural network-based translation could be to think of a fluent speaker in another language that would see a word, say “dog”. This would create the image of a dog in his or her brain, then this image would be associated to, for instance “le chien” in French. The neural network would intrinsically know that the word “chien” is masculine in French (“le” not “la”). But, if the sentence were to be “the dog just gave birth to six puppies” , it would picture the same dog with puppies nursing and would then automatically use “la chienne” (female form of “le chien”) when translating the sentence.

Because of this approach, sentences that are generated from a neural network based machine translation are usually better than statistical machine ones but also sound more fluent and natural, as if a human had translated them and not a machine.

Source: Microsoft

Machine and manual translation – 1

Machine translation is also known as Computer Aided Translation, is basically the use of software programs which have been specifically designed to translate both verbal and written texts from one language to another.

Trados is one of a few computer-assisted translation tools (CAT tools). Its primary function is to allow translators to reuse translations. SDL purchased Trados a few years ago, and their products are generally branded now under the name of « SDL/Trados ». The advantages of using machine translation include the fact that you can make documents in several languages easily.  Generally, it is rather useful for specialized texts (medical, technical, legal), I think. In my opinion any CAT tool is good for those parts of texts that repeat: if you have to translate extracts from business registers, school-leaving certificates, birth certificates, legal records or other such documents, then a CAT tool will do good. One extremely good thing is that Trados keeps the original formatting, so you usually don´t have to deal with the visual form of a document.  When more people work on large projects, it helps to use the same terminology and thus increase the overall quality of translated documents. One of advantages of CAT-Tools is terminology handling.  A good Multiterm-glossary can be extremely useful for legal documents, too. If you are dealing with repetitive texts that are crawling with specific terminology, then this software is the tool for you too.  Next advantage is that with CAT-tools you cannot accidentally leave out a sentence – something that can all too easily happen when overwriting. Another advantage of Trados  is an excellent way to review other people’s texts.

The main disadvantage of using machine translation is his cost, but you can leverage it in a very short time. I heard several people have said that Trados (or any CAT tool) is no good for creative texts, literary translation etc. The other disadvantage is the accuracy of translated material (text) depending on word ordering of original text.

Nuances, cultural differences, and vocabulary that is very local need to be translated by a person. Systematic and formal rules are followed by machine translation so it cannot concentrate on a context and solve ambiguity and neither makes use of experience or mental outlook like a human translator can.