Machine Translation at Volkswagen AG

This is a guest post by Jörg Porsiel, who manages MT at VW, that provides some perspective on the value of MT in the context of a large global enterprise's communication and information distribution needs. VW, like many other truly global enterprises, needs a large variety of business content and product information to flow easily, and quickly, to enable rapid response to emerging and ongoing business situations and needs.

We can see from this viewpoint that the global enterprise has a very pragmatic and dispassionate view of this technology, which is simply seen as a tool to enable and improve information flow, in environments that are truly multilingual, and that require large amounts of content to flow instantly where needed to enable forward business momentum.

We also see that this is a use-case scenario where the need for on-premise installation is critical and necessary for deployment for both security and performance reasons. Some may also be surprised that this MT activity has been ongoing for over a decade. This is yet one more proof point that hundreds of millions of words are being translated by MT at VW and other companies like them, who I am sure also use professional translation services for some content. However, we should reasonably expect that the bulk of the flowing translation needs are being done by MT here, and at many other global enterprises.

For those who can read German, there is a much more detailed overview of  MT at VW at this link. 19 pages in fact.

 The emphasis in the post below is all mine.

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Initial situation

Volkswagen AG is the world’s largest car manufacturer, selling more than ten million vehicles in 2016. More than 625,000 people work for the Group’s thirteen brands at more than 120 locations worldwide, with approximately 280,000 in Germany alone. The headquarters of the brands VW Passenger Cars, VW Commercial Vehicles, Audi, Porsche and MAN are located in Germany. Bentley has its headquarters in Great Britain, Bugatti in France, Škoda in the Czech Republic, Seat in Spain, Ducati and Lamborghini in Italy, and Scania in Sweden.

  

Countless teams in a variety of disciplines work on projects around the clock simultaneously or sequentially, distributed among various time zones around the world and on all continents. In addition to German, the primary languages for communication during this work are English (as lingua franca), Spanish, Chinese and French, but of course Brazilian Portuguese, Italian, Czech, Russian and Polish and many other languages are used as well. This results in a constant stream of information – millions of emails and terabytes of data from, for example, systems for simulation, diagnostics and infotainment – circulating all day, every day, within the Group-wide Intranet or entering it from outside.

To facilitate, and especially to accelerate, the exchange of information in numerous languages across continents, it was decided in 2002 to introduce machine translation into the company. In the interest of data security, the application had to be available strictly within the VW Intranet, which is accessible throughout the Group. This was intended to close the gaps in security created by the use of such programs in the Internet. Volkswagen AG has been operating a rule-based system from the German company, Lucy Software and Services GmbH. The functionality of the system has been expanded since then, and its quality has steadily improved, for example, through the addition of in-house terminology in collaboration with various departments at Volkswagen.

Currently, eight language pairs covering the most important Group languages are available. Additional functionality includes the translation of entire documents, for example, in MS-Office, XML and PDF formats, as well as web pages. There is also an interface to a web service enabling systems to access machine translation. These systems, such as vehicle diagnostics, are generally characterised by a very high volume of data in various languages. 

Why use machine translation?

The speed at which machine translation works and produces results is a significant advantage. Of course, this speedily generated output must first be considered independently of the quality of the translation. The speed – dramatically faster than human translation – proves itself to be an important component in the optimisation of multilingual communication processes in the business environment, especially in conjunction with terminology for each specific field and accompanied by upstream and/or downstream quality control. The fast availability of such raw or gist translations in conjunction with the previous knowledge and expertise in the field of the recipient generally results in significantly faster decision-making processes. Although the quality of a machine translation is in general lower than that of a human translation, depending on the quality of the source text, it is often sufficient for taking specific measures. In the event that the quality of the machine translation is not adequate, it can be improved by pre-editing the source text and/or post-editing of the output as necessary.

Management of Expectations

The larger and more international a company is, and the more heterogeneous the workforce, the more important it becomes to develop a communication concept which is implemented prior to the introduction of, and in support of, machine translation in order to familiarise the users with its advantages and disadvantages. Such a concept is necessary to ensure the long-term acceptance by the users because the better the users’ understanding of the application’s strengths and weaknesses, the greater the acceptance and resulting level of use as well as constructive cooperation in the further development of the service within the company.

At a company as large as Volkswagen and with such a large number of potential users, it is impossible to satisfy everyone. In other words, not every wish can be satisfied, nor can all file formats, language pairs and technical fields can be provided at the same level of quality. This is neither technically possible nor, from the perspective of a cost-to-benefit analysis, economically viable. Thus, the aim of providing such a service can be only to satisfactorily meet a “representative average” of the statistically expected needs with suitable quality within the limits of the available personnel and finances.

The management of expectations is also important for explaining the uses of machine translation in day-to-day work to groups with experience in (computational) linguistics and translation and, if necessary, to counter the reservations regarding translation quality and, in particular, job security. As part of such a concept, it must also be explained what machine translation can do especially well and under what conditions it must be used, for example, in conjunction with specialised terminology management, controlled language and, as needed, post-editing.

But on the other hand, it must be clearly emphasised that machine translation is neither a panacea nor an all-purpose tool for every translation task, which might thus be intended to eliminate jobs, but rather for general cost reduction. More importantly, it must be emphasised what machine translation cannot do, and why: for example, certain types of text are not suitable for machine translation or are (or could be) crucial for legal reasons.

Furthermore, it must be explained, for example, with a cost-to-benefit analysis, that machine translation cannot work in the long term without qualified and continuous support from specialists, and that there is no such thing as a “one-size-fits-all” solution. In other words, the idea that a one-time installation of a machine-translation program without supporting measures such as IT support, terminology, controlled language and so on is adequate to enable translation from every language into every language, regardless of the subject area, type of text and target group, is out of touch with reality. This should also make it clear that even just the technical operation and IT support require continuous and secure financing, without which the introduction of such a program is not prudent.

Further development

In addition to the continuous improvement in the quality of the translation results through the inclusion of additional terminology from a multitude of different technical fields, it is planned to link the service by means of an interface with, or to integrate it into, still more systems. Furthermore, it is planned to extend the available languages to Chinese, Portuguese, Czech and Polish, for example.

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Jörg Porsiel is a Machine Translation Project Manager at Volkswagen Headquarters in Wolfsburg, Germany. A translation graduate of Heidelberg University, he has also studied in Brussels, Edinburgh and Metz. Since 1992 he has been working in translation, terminology management and foreign language corporate communication. He started working for VW in 2002 in the field of controlled language and as of 2005 is responsible for the Group’s internal machine translation service.