MACHINE TRANSLATION- EVOLUTION NOT REVOLUTION -

Jennifer A. Brundage

SAP AGP.O. Box 14 61, D-69185 Walldorf

Germanyjennifer.brundage@sap.com

AbstractThe continuous trend towards globalization means that even the most modern of industries must constantly re-evaluate its strategiesand adapt to new technologies. This not only involves living up to the demands set by the product life cycles but also to findsolutions satisfying additional internal needs. As a long-time supporter of MT and TM technology, SAP has shown that it can makeproductive use of competitive, commercial NLP products. As a first step, an integrated solution using TM together with MT wastargeted. Having implemented different solutions for two types of documentation, the focus is now on not merely to integrate othertechnologies (e.g. terminology mining or controlled language) but to provide a uniform solution for processing any type of text. Thisinvolves not only supporting the needs of technical writers and translators but of all employees in their multilingual workingenvironment.

KeywordsMachine Translation, MT evaluation, MT entry generation, NLP tools integration, workflow tool

NLP Technology in Documentation and Translationat SAP

From the late 80’s early 90’s, SAP has developed andenhanced its own proprietary terminology database(SAPterm) which is fully integrated in their businesssoftware product. Currently, version 3 is being usedproductively. In-house it is the central source for SAPterminology and can be accessed by all SAP employeesvia a Web interface.The MultiLingual Technology (MLT) group originallystarted out as a purely Machine Translation group backin 1990. Once its first project to introduce MT forGerman-to-English as an in-house service for thespecialized translators was realized, the group very soonrealized that more could be achieved with suchtechnology. The success of this initial project thentriggered several others.With the importance of the SAP products in the marketgrowing, the number of customer notes1 (or short: notes)increased accordingly. This posed a problem since thetranslation had to be available within 24 hours uponrelease of the source text for the priority 1 notes (SupportLevel Agreement). These parameters made it impossibleto succeed using the conventional methods. In 1994, acooperation with the MT vendor to implement aworkflow tool combining Machine Translation andTranslation Memory for the translation of notes wastherefore initiated. With this tool, it was possible for 4-6people to easily translate some 100 to 150 notes (onaverage 96 words) per day. Once the entire process wasset up, thoroughly tested and the technology proved to bestable, the customer notes translation was gradually

1 As one of the world’s largest software houses, SAP AG has acomplex system for informing customers about bug fixes inand distributing minor source code corrections for the R/3System, as well as for distributing additional functionality, bestpractices and other customer requests.

outsourced starting in 1996 (Brundage, McCormick &Pyne 1997).SAP’s popularity in other than the English and Germanspeaking markets, of course, led to the support of otherlanguages having to be extended. Thus, MachineTranslation for English-to-French was introduced in 1994as well. The user lexicon currently contains some 60 000entries. Since none of the commercial MT systemssupports all languages and large part of the documentationwas being re-used, alternatives needed to be found. In1996, standard commercial Translation Memorytechnology was introduced to SAP on a large scale. As thedemand cropped up, MT for English-to-Spanish was set upin 1999. To shorten ramp-up time, a new method was used.All English source words maintained in SAPterm without aSpanish target equivalent were exported and existingtranslation memories for that language pair were searchedfor possible translations. The proposals found in thememories were passed on to the specialist translatorstogether with the context information. These then verifiedor changed the proposals. The result was a list of bilingualterm pairs. This list could then be imported into the MTsystem (overall time saving: 20%) and also serve as a basisfor completing the entries in SAPterm. The MT userlexicon now contains some 58 000 entries.The MT system used for German-to-English not beingY2K-compliant represented a new challenge for the group.Not only had the follow-up MT system to be evaluated andthe terminology migrated, but a completely new workflowtool for the notes translation had to be designed,programmed, tested and implemented (Wells 2001). Thisalso involved a rollout plan for the agency translating thecustomer notes. One huge advantage of this new solution isits user friendliness. The translator controls the entireprocess via a GUI (see Fig. 1) instead of Unix commandsand can use MS Word, which is automatically launched, aseditor instead of emacs.

Figure 1: End-User GUI for Managing Notes Translation

Also, the administrator can easily configure theapplication so that the agency is provided with therequired files from the various locations. A loggingmechanism makes it possible to monitor the process real-time or at a later time.

Figure 2: Interface for Administrating the File Transfer

Currently, two agencies at three locations are dealingwith the notes translation. From the start of this project tothis very day, the volume of notes being released eachday has increased to 450-550 (approx. 180 new and 350updates). The MT user lexicon for German-to-Englishcontains some 75 000 entries.

New ResponsibilitiesWith SAP growing at the speed it has over the last 5years, one of the company’s current focuses is to reducecosts, but at the same time ensure quality and time-to-market shipping. One means to achieve this is tointroduce tools for certain tasks that can be automated.Currently, SAP supports 30+ languages. And with theimportance of the market and the multiplication ofproducts offered, the volume of documentation increases.For those languages not supported by the commercialMT systems, a translation memory system is usedtogether with automatic terminology recognition. Thissolution proved effective and led to savings of up to40%. Where Machine Translation could be used togetherwith TM, savings reached 60%. However, SAP not onlyconcentrates on using translation tools but also other

NLP tools. The volume of documentation SAP has to dealwith in the various languages makes it difficult to manage.Are there pieces of documentation that can be re-used?How to ensure terminology consistency? How to enforcethe defined standards and guidelines?The fact that the development of the terminology databaseSAPterm and of the proprietary translation tools are nowpart of the MLT group is a great advantage for tacklingthese issues.As generally known, translation quality - not only MToutput – depends heavily on the quality of the source text.So if SAP succeeded in improving the quality of theirsource documentation (currently German and English), thecosts for the follow-up corrections could be reduceddramatically.In addition, if unknown or new terms could be identified,defined and maintained before the documentation isreleased for translation, this would reduce the number ofinquiries by the agency; resulting in not only money beingsaved but also time - another factor not to beunderestimated. Ideally, the terminology should bemaintained only once and then be available for all NLPtools requiring this kind of resources. The challenge is notonly that the various NLP tools need a different set offeatures but they also require these being available indifferent formats for usage in different databases.To top things off, the different document types are writtenin different formats. A big chunk of the SAP systemdocumentation (such as customer notes, release notes, F1help) is written in ITF, a proprietary format, for which aconverter to and from RTF has been written, thus makingthe usage of NLP tools easier. Other formats used are thestandard Word documents, PowerPoint export files, andHTML/XML.

Ensuring Documentation QualityTwo approaches were taken at SAP to tackle this problem.

Copy EditingCopy editing being a traditional “human” approach, it wasthe most obvious first step to ensure the adherence toSAP’s welldefined Standards and Guidelines. Quitequickly, however, it revealed some considerabledisadvantages. Only small portions could be dealt with inthe given time frame. Being a manual process, it proved tobe too time-consuming and too costly.

The SKATE projectAs an alternative and extension, the introduction ofControlled Language was discussed. As a result of this, thedevelopment phase of the SKATE project was initiated in2000 with the support of the Board of Directors. MLT hasalways been of the opinion – and still is – that it is notnecessary to use Controlled Language to make MT work.Without doubt, however, it can improve the output quality.SKATE stands for SAP Knowledge Authoring – TextEnhancement and is the project dealing with theintroduction of Controlled Language tools at SAP. Themain objective of this project is to reduce ambiguity,redundancy, size, and complexity of the German andEnglish texts since these are the source for all otherlanguages. As a result, the texts should be more consistent,easier to understand and therefore to translate. Againleading to reduction of costs for the translation into up to30+ languages.

Having evaluated the market in 1999-2000, SAP decidednot to introduce an off-the-shelf product, but rather set upa project using an existing project prototype, in whichtheir requirements could be considered. The advantagesof this semi-automatic solution are many. Firstly, the toolcan be used by the authors themselves making themaware of the stylistic, grammatical, spelling, andterminological issues of the language they are writing in.In addition, they have the defined standards andguidelines at their fingertips and do not have to look upthe various rules themselves. In addition, it is integratedinto MS Word (see Fig. 3), the standard editor at SAP.Having a tool to support them for the entire process,larger volumes with a certain quality level can bereleased in a set time frame.

Figure 3: The Skate Tool at Use

SAP is planning to go productive this year for bothGerman and English. This means, that not only authorswriting either in English or in German can benefit fromthis technology, but also translators when translating intoGerman or English. A third type of customer for this toolcould be a manager of translation project, for example.Using this tool allows logging certain information on thetext being checked. Therefore, useful information can bequickly provided for the project manager performing agoods receipt quality check when the translation isreturned from an agency or when mass data has to bechecked just before release deadline.SAP has an extra-ordinary plan - running the SKATEtool on raw MT output, MLT hopes to find out how post-editing could benefit from this language checkingtechnology.

Terminology ManagementCompanies are more and more realizing that terminologyis an important investment. Consistent terminology is animportant factor when it comes to acceptance by thecustomer.Defining and maintaining terminology is a time-consuming and costly task. More so, when you are usingseveral tools requiring different formats.

Standard Exchange Format - OLIFThe initiative to define a standard format for theexchange of terminology between NLP tools – inparticular MT systems – has its origin in the EU-fundedproject OTELO, from which a first version resulted.Soon after the OTELO project ended, the OLIF

consortium, headed by SAP, was founded. A new XML-compliant version of OLIF, has become available in April2001. For more information on OLIF, please refer towww.olif.net and Lieske, McCormick & Thurmair. (2001).

Terminology MiningAnother challenge SAP constantly faces is thedevelopment of new industry solutions or add-ons to theirstandard business software. This of course involvesidentifying and defining new terms. One means to catchthe terminology that slipped through when thedocumentation was written is to use a Terminology Miningtool. These tools are designed to analyze the text sentence-by-sentence and propose potential term candidates, eithersingle words or multiword expressions. SAP started aproject for both German and English in 1999/2000.Having completed the prototype testing, development isnow under way. The goal is to go productive for the twosource languages this year. SAP is using a commercial toolto identify potential candidates but has stuck some filterson top to improve the results. These filters, for example,identify duplicates in form of inflectional variants andreduces the proposals to one, or eliminates multi-wordscontaining adjectives belonging to the general vocabularyof the respective language.

(Semi-)Automatically Generating MT EntriesThis refers to a component being developed in theframework of the EU-funded project TQPro (seewww.tqpro.de for more information). The objective here isto fill up potential term candidates (e.g. identified by aterm mining component) with lexical information neededby MT systems. The list of bilingual terms in OLIF formatis then automatically compared against the MT lexicon tofilter out the really new terminology. The challenge here isthat the various MT systems treat context informationdifferently or not at all. Can an automatic compare be100%? How are identical entries that differ only withregards to subject area to be treated? The answer isprobably, No. The compromise one will have to make – atleast, in a first version- is that only basic features arechecked. The final bilingual term list is then imported intothe MT system in question. The recommendation is tocheck the imported list before permanently storing them inthe MT lexicon.Other terminology-related approaches are also taken. Forexample, a terminology checking tool that can be used aspart of the SKATE project or when checking the contentsof translation memories and target documents returnedfrom agencies.

The Flexibility/Integration Capability of CommercialNLP ToolsIn the past, the NLP tool vendors – particularly the MTvendors – were very reluctant about opening up theirsystems for integration into environments other than theirown. With the prospects the popularity of Web opened up,the NLP vendors suddenly realized that integration couldindeed be attractive.It were the TM vendors who made a first – and verysuccessful – attempt in opening up their systems forintegration into customer environments, providing adetailed description of their APIs.Various projects – cooperation between customer andvendor or EU-funded – were initiated. At SAP, the

workflow tool for the notes translation is to be mentionedhere, where the MT vendor provided a dll interface forintegration. Also, in the EU-funded project OTELO,open API was an issue, which is further pursued inTQPro.Regarding the support of OLIF, the major tool providersmade a commitment to support it. This includes SailLabs, Logos, Systran, Trados, Xerox, and many others.

Facing the New ChallengeThis paper might make the impression of being simply aconglomeration of subjects and aspects, but all thesetopics are a piece of the puzzle and make up every daywork of the MLT group. The group not only concernsitself with contents-related issues (linguistic,lexicographical, quality-related) but also with technicalissues (customizability, scalability, integrationcapability). Every one of them is very important from auser’s point of view like SAP, where a large range oflanguages has to be dealt with. The MT systemsavailable on the market not all support the samedocument formats or all languages. Even where the samelanguage pairs are covered, there can be considerabledifferences with regards to output quality, terminologymaintenance, and customizability. So, there should be theopportunity to choose the best system for the setrequirements. Open APIs and OLIF as terminologyexchange format will be a definite plus, especially whenintegrating an MT system into a workflow toolcontaining a terminology mining component, forexample. The potential term candidates being availablein OLIF format can then be directly imported into theMT system.The MLT group has recently been asked to set upMachine Translation for English-to-Japanese andJapanese-to-English to support with professionaltranslation as well as with the communication for callcenters (customer and internal messages and notes, etc.).Triggered by this opportunity, MLT is hoping to realizean all-comprising, platform-independent authoring andtranslation environment, regardless of what documenttype is to be processed in which language and for whatpurpose. All tools are run and maintained centrally, theusers having remote access to whatever services theywant to use. The front-end user interface of the workflowtool should be customizable to fit any user profile.Various scenarios are possible. A technical writer couldmake use of the SKATE tool, the terminology miningcomponent, and could include a dictionary lookupfunction in his view. In cases, where he re-uses textobjects from previous versions and some terms havebeen changed in the meantime, he could also activate anIntelligent Find and Replace tool. A translator, on theother hand, could use TM and MT technology togetherwith terminology mining. Since SKATE will be availablefor both German and English, it can also be used in thetranslation process, when translating from German intoEnglish, or vice versa. Other components developedwithin TQPro, like translatability (Underwood, Jongejan2001) or completeness check, could also be activatedupon request.As additional language pairs need to be supported, newMT system might be added. And where no MT system isavailable or does not meet the requirements regardingoutput quality, customizability, or integration, the huge

volumes of bilingual segments stored in SAP’s translationmemories could serve as a valuable input for an example-based approach to MT.When this paper was submitted, the MLT group was in theprocess of evaluating commercially available MT systems.The purpose was not only to update the group’s knowledgeregarding this type of technology, but also to evaluate theoutput quality for language pairs to be introduced at SAP.In addition, the group might be forced to replace the MTtechnology used for some of the language pairs. Apragmatic approach was chosen for the evaluation. Apartfrom some functional and technical aspects, the first mainfocus is on the output quality. A corpus containingdifferent documentation types (user manuals, officialmails, customer notes, messages, etc.) was set up for eachsource language involved. These texts were then translatedusing the various MT systems as delivered, i.e. only usingthe system dictionaries. The MT output was then viewedwithout reference to the source text and rated. The profileof the people viewing and rating the output ranges fromMT specialists to persons unfamiliar with MT output. Thegrades that were used are the following:1 – Good translation2 – OK3 – Understandable4 – Understandable from context only5 – Not usableThen the MT output was closely searched forterms/phrases to be maintained in the respective MTsystem. Once the terminology maintenance was completed,the texts were retranslated and the output evaluated usingthe same rating system.One very important aspect to be considered in case an MTsystem has to be replaced for an already productivelanguage pair, is the size, the quality, and the coverage ofthe existing user dictionary. And in this context – howeasily can the existing dictionaries be transferred to thenew MT system (if at all) and how much information willbe lost.The results of this evaluation and the experiences madewithin will be presented at the MT Summit.

References[1] Brundage, J., McCormick, S., Pyne, Chr. 1997:Managing Distributed MT Projects Today – A NewChallenge, MT Summit VI Proceedings; pp.58-63.[2] Lieske, C., McCormick, S., Thurmair, G. 2001: TheOpen Lexicon Interchange Format (OLIF) Comes of Age,MT Summit VIII Proceedings, pp.?[3] OLIF homepage: www.olif.net[4] TQPro homepage: www.tqpro.de[5] Underwood, N., Jongejan, B. 2001: TranslatabilityChecker: a tool to support the decision on whether to useMT, MT Summit VIII Proceedings, pp.?[6] Wells, J.: Notes Translation Using MT and TM???, notyet published.