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Integrating Machine Translation with Translation Memory: a Practical Approach

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Integrating Machine Translation with Translation Memory: a Practical Approach Integrating Machine Translation with Translation Memory: A Practical Approach Panagiotis Kanavos Dimitrios Kartsaklis Freelance Technical Translator Freelance Software Engineer K. Varnali 20, K. Aharnai, 136 71 M. Mpotsari 46, Petroupolis, 132 31 Athens, Greece Athens, Greece [email protected] [email protected] Abstract Computer-Assisted Translation (CAT) vendors have been reluctant or uninterested in investing further in The purpose of this work is to show how MT technologies, with the exception of Google’s machine translation can be integrated into MT service which is offered as a plugin to a few professional translation environments using CAT programs. two possible workflows. In the first workflow We will demonstrate that MT–when com- we demonstrate the real-time, sentence-by- sentence use of both rule-based and statistical bined properly with Translation Memory (TM) machine translation systems with translation technologies–is actually a very useful and produc- memory programs. In the second workflow we tive tool for professional translation work, contrary present a way of applying machine translation to the common belief that MT is still restricted to to full translation projects beforehand. We also draft Web translations of low quality. Furthermore, compare and discuss the efficiency of statis- we will show many possible ways to combine MT tical and rule-based machine translation sys- systems with TM workflows, as well as the ability tems, and propose some ideas about how these systems could be combined with translation to use both rule-based and statistical MT systems. memory technologies into a unified translation application. 2 Methodology Our workflows and methodologies are divided into 1 Introduction two general categories: the on-demand, sentence- by-sentence application of MT, and the one-time ap- Machine Translation (MT) has been evolving plication of MT into the whole translation project. rapidly, drawing attention from other professionals In both cases, we use and combine various tools, besides researchers. However, we cannot claim that MT systems, even operating systems and platforms, it is generally accepted as a useful and productive to provide a unified translation environment which technology by the professional translation industry. most professional translators are familiar with. The On the one hand this is partly due to unrealistic ex- CAT programs used are Swordfish II (a Java appli- pectations as to what MT should be able to do and cation) in a Linux operating system, Dej´ a` Vu X in how it should be able to achieve it. On the other a Microsoft Windows environment, and Wordfast, a hand, it is true that the MT community focus mainly Microsoft Word macro template, also used in a Win- on research, overlooking practical issues. This is dows operating system. The MT systems used are reflected in various online surveys and forums re- the Moses statistical system (Koehn et al., 2007), lated to professional translation, where translators and a rule-based edition of Systran1. and language service providers consider MT an ex- 1Although the Systran edition that was available for our lan- perimental technology suitable for draft translations guage pair was rule-based, newer editions use a hybrid technol- of e-mail messages and web pages. To this respect, ogy incorporating both statistical and rule-based elements. Ventsislav Zhechev (ed.): Proceedings of the Second Joint EM+/CNGL Workshop “Bringing MT to the User: Research on Integrating MT in the Translation Industry” (JEC ’10), pp. 11–20. Denver, CO, 4 November 2010. 11 JEC 2010 “Bringing MT to the User” Denver, CO For the purposes of our research, we chose to Swordfish II CAT program, which offers a flexible translate two technical books in the field of infor- plugin architecture allowing the user to connect to matics from English to Greek. The first one was an external applications or scripts, and Wordfast Clas- instructive guide (referred to in this paper as Book 1) sic, which provides a feature for connecting to MT to a popular desktop application, which contained a systems. Although both programs are used under the lot of steps for accomplishing specific tasks and had same method, their combination with MT systems a certain amount of repetitive or similar text. This varies significantly because of the operating systems kind of text is ideal for work in a TM environment involved and the MT systems they can be connected since the TM yields a lot of exact and fuzzy matches. to. Swordfish was used with Moses, while Wordfast The other book was an academic one (referred as was used with the Systran engine. These particu- Book 2), covering more complex and theoretical top- lar combinations were chosen based on the tools we ics in the field of Computer Science, and contained had available and some practical factors (explained very few repetitive sections or step-by-step instruc- in the following sections) concerning the integration tions. Such texts do not benefit particularly from of each CAT tool with the corresponding MT sys- TM matches, although they do benefit from other tem. features offered by CAT tools, such as concordance 2.1.1 Swordfish’s translation environment searches, consistency in style and terminology, auto- matic number and tag checks, etc. The configuration Swordfish’s translation environment offers all the of the CAT programs was almost identical: we used usual features met in CAT tools, such as the the same TM which contained 140,000 translation translation grid where the segmented text is pre- units, the same terminology database containing an sented for translation, the TM database results average of 30,000 entries, and the same segmenta- pane, the terminology database results pane, and tion rules. All our resources derived from previous an Auto-Translation pane, which shows the results translations in the Informatics domain. Also, the TM of the program’s attempt to assemble translations fuzzy threshold was set to 70% for all programs. using matches from the TM and the terminology We translated both books in our three TM-MT databases. This is of special interest for the purposes combinations and obtained measurable results in of our work since it is the closest native feature the terms of time spent, translation quality, and overall application offers to MT. Another feature we will efficiency of our workflows. It should be noted that use for comparison is the application’s GTranslate the books translated with our workflows were real plugin which can be invoked by the user on-demand assignments from a publishing house and they are for automatic translation using Google’s MT engine. now under publication. We divided each book in six The workflow presented by Swordfish is typical equal parts (two for each combination) and we cre- of CAT programs: ated separate translation projects for each TM-MT • Import, conversion, and segmentation of the combination. Then, in each combination, we trans- source files into Swordfish’s native format lated one part using only the TM and another part (XLIFF) using both the TM and the MT, keeping track of the time spent so we could measure the effectiveness of • Connection of TM and terminology databases the MT. Productivity is expressed in target words per into the project hour. • Pretranslation of the entire project using the 2.1 Applying MT on demand during the connected TMs (optional) translation process • Translation of the project, segment by segment The ability to use MT engines from within CAT pro- grams is largely dependent on the CAT program it- • Export the project into the original file format self. Thus, there has to be a way to connect the MT engine to the TM application and invoke it at will Swordfish integrates seamlessly with any external for selected segments. For this reason, we chose the application or script due to its plugin architecture. 12 November 4th, 2010 Panagiotis Kanavos and Dimitrios Kartsaklis This is a key feature that let us customize our work- flow to a great extent. We created a custom Python plugin that manipulated the current segment. The exchange file created by Swordfish is in the XLIFF format (XML-based), so the translatable text can be easily extracted using Python’s XML modules. The XLIFF project format used by Swordfish is very flexible for applying MT since it does not re- quire any special conversions or additional prepa- ration steps. Furthermore, integrating the MT re- sults into Swordfish’s environment was seamless, since we could automatically create an appropriate XML element for each machine-translated segment and make the translation appear into the TM matches pane. 2.1.2 The Moses Statistical MT System Moses is a statistical MT system developed by the EuroMatrix2 and EuroMatrixPlus3 projects and var- ious institutions and universities and is licensed un- der the LGPL (Lesser General Public License). Such systems require large corpora of text for training and creating phrase tables and language models which Figure 1. Steps for preparing a Moses MT system are used during the automatic translation (decoding). Translators can benefit from such systems since they of our MT system was not hindered by the limita- already have available considerable amounts of texts tion in the amount of sentences because the train- in the form of TMs. All CAT programs offer an op- ing corpus was very domain-specific to our transla- tion for extracting the entries of the TMs into the tion project. This issue is extremely important when industry-standard TMX format which can be easily combining MT and TM technologies and it is ana- manipulated for preparing the text corpus. lyzed in greater depth in Section 4. Moses, along with the required utilities and scripts, requires a Unix environment and a fairly ca- 2.1.3 Integrating Moses into Swordfish pable computing system.
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